JOURNAL

Je te regarde et mon cœur s'affole, tu bouges si joliment, on a l'impression que tu danses, puis mes yeux tombent dans les tiens et c'est le vertige. Comment des yeux humains peuvent être aussi bleus ? Tu me souris je te tends les bras oh ton odeur !

#erotica

The Short Version

PlantLab's API now returns a reliability_score field on every diagnosis. A number from 0 to 1 telling you how likely the answer is to be correct on this specific image. It replaces the old diagnostic_confidence and safety_classification fields, which were rule-based guesses that I never trusted. The new score is much better at flagging the diagnoses that turn out to be wrong – especially on the hard cases, which is where you actually need it. Schema bumped from 1.x to 2.0.0. If you're integrating with PlantLab today, the migration is a one-line change.

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The problem with “confidence” fields

Most diagnosis APIs return a confidence number along with each answer. PlantLab did too. For every condition the model spotted, the response included a confidence value between 0 and 1. On top of that, the response also carried two derived fields. diagnostic_confidence, a single overall trust number, and safety_classification, a three-way bucket of high, moderate, low.

Those derived fields were a heuristic. A small handful of rules that mostly looked at the top condition's confidence and rolled it up into a number. Heuristics work fine when the problem is simple. They fall apart when the failure modes are subtle.

In real traffic, the cases that matter are the ambiguous ones – photos where the answer isn't obvious from the image alone, and a single rule isn't enough to capture how confident the diagnosis really is. That's the slice where a trust signal earns its keep, and the slice where a rule-based composite tends to break.

A trust signal that works on the easy cases and stops working on the harder ones isn't really a trust signal. It's a confidence display.

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What reliability_score does differently

reliability_score is a single number from 0 to 1 that estimates how likely the top diagnosis is to be correct on this specific image. Higher is better. Below 0.3 is a clear “double-check this one.” Above 0.7 is “the system is confident and the confidence holds up.”

It doesn't replace per-class confidence. Those still tell you how strongly the model picked each individual condition. What reliability_score adds is a separate answer to a different question – “is the entire diagnosis trustworthy on this particular image, or is something off?”

The analogy I keep coming back to: a junior diagnostician who always gives an answer, and a supervisor who looks over their shoulder. The supervisor doesn't redo the diagnosis. They judge whether each one looks trustworthy. The old diagnostic_confidence was a checklist the junior filled in themselves. reliability_score is the supervisor.

I held the new score to a higher bar than the old composite. On the ambiguous cases, it does a much better job of flagging the answers you should double-check before acting on them. On the easy cases, both fields agree – which is the only place they were ever going to agree, and not where the score earns its keep.

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What changes in the response

If you're integrating with PlantLab today, here's what your code currently sees:

{
  "request_id": "550e8400-e29b-41d4-a716-446655440000",
  "schema_version": "1.2.0",
  "success": true,
  "is_cannabis": true,
  "is_healthy": false,
  "growth_stage": "flowering",
  "conditions": [
    { "class_id": "magnesium_deficiency", "confidence": 0.85 }
  ],
  "diagnostic_confidence": 0.85,
  "safety_classification": "high_confidence"
}

After the upgrade, that same image returns:

{
  "request_id": "550e8400-e29b-41d4-a716-446655440000",
  "schema_version": "2.0.0",
  "success": true,
  "is_cannabis": true,
  "is_healthy": false,
  "growth_stage": "flowering",
  "conditions": [
    { "class_id": "magnesium_deficiency", "confidence": 0.85 }
  ],
  "reliability_score": 0.91
}

Two fields removed. One field added. The rest of the response is identical.

reliability_score is omitted when the API doesn't return a condition diagnosis – for example, when the photo isn't of cannabis, or when the plant is healthy. In those cases, there's no diagnosis to score for reliability, so the field doesn't appear. Treat its absence as “no score available” rather than “low score.”

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Migration

The change you make depends on what you were doing with the old fields.

If you were displaying diagnostic_confidence to a user, swap to reliability_score. The semantics are the same direction (higher is better, both 0-1), and the new value is more accurate.

If you were branching on safety_classification strings, pick thresholds on reliability_score instead. A reasonable starting point: above 0.7 is “Confident,” 0.3 to 0.7 is “Uncertain,” below 0.3 is “Low confidence.” Your application can use whatever cutpoints make sense – the score is a number, not a string, so you have full flexibility.

If you were ignoring the old fields entirely, the upgrade is automatic. Remove your code that references diagnostic_confidence or safety_classification (it'll get null going forward) and you're done.

The Home Assistant integration shipped a new release the same day as the API change, so existing HA users get the new sensor automatically. If you're using a custom integration, update it before the next API deploy if you can – sensors that read the removed fields will return null until the integration is updated.

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Why a breaking schema, not deprecation

I considered keeping diagnostic_confidence and safety_classification as deprecated fields, returning the old values alongside the new score for a release or two. It would have spared everyone a migration step.

But it forces consumers to choose between two trust signals that can disagree. The old composite says “low confidence” on a photo where the new score says 0.95 – which do you trust? Worse, deprecated fields stick around for months, and integrators keep reading them instead of migrating. That's basically the entire failure mode of deprecation.

Cleaner break, single migration, no ambiguity. Schema bumped to 2.0.0 to make it loud. If your integration was on schema 1.x, you'll start getting 2.0.0 responses the next time you call the API. Field changes are documented above.

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What's next

reliability_score ships as v1. The field semantics stay stable: a 0 to 1 trust score, present on diagnoses that returned a condition prediction. Future improvements land behind that contract. Same field, more accurate values, no code changes on your end.

If you migrate now, you're done with the migration.

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PlantLab is free to try at plantlab.ai. Three diagnoses a day, results in milliseconds. The full API documentation, including the OpenAPI spec, lives at plantlab.ai/docs.

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FAQ

Do I have to migrate immediately?

You'll start receiving schema 2.0.0 responses the next time you call the API. If your code reads diagnostic_confidence or safety_classification, those reads will return null. If your code branches on those fields, your branches will fall through to whatever default path you wrote. So the migration urgency depends on what your code does with null values – some integrations will degrade gracefully, others will break.

Is reliability_score the same as confidence?

No. confidence (still present in conditions[] and pests[]) is the model's per-class probability for one specific class – “how confident am I that this leaf shows magnesium deficiency?” reliability_score is a separate signal that estimates how likely the entire diagnosis is to be correct on this image. The two answer different questions, and you can use both.

What does it mean when reliability_score is missing?

The score is only computed when the API returns a condition diagnosis – that is, when the photo is cannabis and the plant is unhealthy. For non-cannabis photos or healthy plants, there's no condition prediction to score, so the field is omitted. Treat absence as “no score available,” not as a low score.

How is this different from just thresholding on confidence?

Per-class confidence values are the model's individual outputs. They tell you which classes were predicted strongly. They don't tell you whether the diagnosis as a whole holds up on a given image. reliability_score answers that broader question, which is usually the one you actually have.

Can I see PlantLab's diagnosis history for my key?

GET /usage returns daily and monthly counts. For per-request lookup, store request_id from each diagnose response – it's stable, returned in both the JSON body and the X-Request-ID header. Use it for support tickets and feedback submission.

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Related reading: – The Work Nobody Sees: How I Ran 47 Experiments to Make PlantLab's AI Better – What goes into making the model more accurate, cycle by cycle – How PlantLab's AI Diagnoses 31 Cannabis Plant Problems in 18 Milliseconds – The full pipeline

The Short Version

PlantLab's AI doesn't ship once and stop improving. Behind every release is a cycle of automated experiments that audit the model's own predictions, find where it struggles, and fix the root causes before retraining. The latest cycle ran 47 hyperparameter experiments, analyzed 1,081 classification errors, and cleaned data across 1.34 million images. This is what continuous AI improvement actually looks like – no buzzwords, just the work.

Most AI Products Stop After Training

Here's something most AI companies would prefer you didn't think about: they train a model once, wrap it in an API, and never touch the internals again. Updates mean prompt tweaks or UI changes. The underlying model, the thing that actually makes predictions, stays frozen.

For general-purpose tools, this is fine. But for plant health diagnosis, where the difference between potassium deficiency and magnesium deficiency is a few pixels of vein color, “fine” means wrong often enough that growers stop trusting it. And they should. A diagnosis tool that's right 90% of the time is wrong one in ten. That's not a rounding error when you're deciding whether to flush your nutrients.

PlantLab takes a different approach. Every few weeks, the model goes through a structured improvement cycle. Not a full retrain from scratch – a targeted investigation that finds specific weaknesses, fixes them, and measures whether the fix actually worked.

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How the model audits itself

The improvement cycle has three phases that feed into each other.

First, find the errors. Run the current production model against its own training data. Every disagreement, where the model's prediction doesn't match the training label, gets flagged for review. In the most recent audit, I ran 109,000 original images through the model and logged every mismatch.

Then, understand the errors. Not all errors are equal. A confusion analysis maps which classes the model mixes up and how often. In the last analysis, I found 1,081 errors across 31 condition classes. But the distribution was revealing: 10 classes had zero errors (solved problems), while potassium deficiency alone accounted for 216 errors, confusing it with nearly every other nutrient class and even spider mites.

Then, fix the root cause. Sometimes the model is wrong. Sometimes the training label is wrong. When you find 53 mutual errors between potassium deficiency and spider mites, the question isn't “why is the model confused?” but “are these images actually labeled correctly?” In many cases, they weren't. Clean the labels, retrain, and the confusion drops.

Then repeat. The model gets better, which means it catches more labeling mistakes in the next audit cycle, which means the next retrain starts from cleaner data. Each cycle produces better data, not just a better model.

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47 Experiments and a humbling lesson

The most satisfying failure in this process came from hyperparameter tuning, which is the process of finding the optimal learning rate, regularization strength, and other training knobs that control how a model learns. I say “satisfying” because it looked like a win at every step until it wasn't.

I built an automated sweep system that tests different hyperparameter combinations on a small subset of data (5% of images, 20 training epochs). It ran 47 experiments across four campaigns, testing at three different image resolutions, over about 23 hours of GPU time. Zero crashes. Clean, repeatable results. The optimal settings were clear: a learning rate of 1e-4, no label smoothing, moderate dropout. I had graphs. They were beautiful.

Then I applied those “optimal” settings to a full training run, all 554,000 images, 150 epochs, and it performed worse than my baseline at every single checkpoint. I stopped 51 epochs in, which is the machine learning equivalent of pulling over and admitting you're lost.

So what happened?

The small-scale sweep and the full-scale training are different worlds. At 5% data and 20 epochs, the model barely has time to memorize the training set, so regularization – techniques that prevent overfitting – doesn't help much. At 100% data and 150 epochs, the model has seen every image hundreds of times, and regularization becomes essential. The settings that were optimal for a quick experiment were actively harmful at production scale.

It's a well-known trap that I walked into with my eyes open: optimizing on a proxy (small, fast experiments) and assuming the results transfer to the real thing (full-scale training). The numbers look scientific. The methodology is sound. And the conclusion is wrong. I knew this was a risk. I ran the full-scale experiment anyway because “it'll probably be fine” is the most expensive sentence in machine learning.

What Actually Worked

I quantified the gap by computing a “regularization score” – a single number that captures the combined effect of learning rate, label smoothing, and dropout. The sweep-optimal settings had a regularization score 150% higher (less regularized) than my proven baseline. That magnitude of change isn't an optimization – it's a regime shift.

The fix was to use the sweep's directional findings (which hyperparameters matter most, which direction they should move) but anchor the absolute values to what had already worked at full scale. I nudged the learning rate up by 20% instead of doubling it. Halved the label smoothing instead of eliminating it. Kept the dropout finding because dropout's effect is per-batch, not scale-dependent.

The result: the scale-adjusted settings matched our best-ever model at epoch 94, with lower volatility throughout training. Not a breakthrough. A boring, reliable improvement. Which, if you've been doing this long enough, is what you actually want.

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The Potassium Problem

The confusion analysis had one standout finding, and it wasn't what I expected.

Across 31 condition and pest classes, potassium deficiency was the single worst performer: 216 errors, confused with magnesium, nitrogen, calcium, phosphorus, iron, spider mites, and bud rot. Potassium deficiency was apparently everything and nothing at once. The top three nutrient confusion pairs (magnesium-nitrogen, magnesium-potassium, nitrogen-potassium) accounted for 30% of all model errors.

This wasn't a model failure. It was a data quality signal.

When potassium deficiency shows 53 errors with spider mites in one direction (top row) and 27 in the other (left column) – a completely different category – the model isn't confused about biology. The training images are. Someone labeled a photo “potassium deficiency” when it actually showed spider mites, or (more often) showed both. Real-world plants don't politely have one problem at a time. A plant stressed by potassium deficiency is more susceptible to spider mites, and the photo shows symptoms of both. Good luck labeling that at 2 AM.

The fix isn't a better model. It's better labels. I built a label review pipeline that uses two independent AI systems to re-examine every flagged image. When both agree the label is wrong, it gets fixed. When they disagree, a human reviews it. This process cleared 4.8% of the training set as mislabeled or ambiguous in the most recent pass.

4.8% sounds small. But 4.8% of 1.34 million images is over 64,000 images that were confidently teaching the model the wrong answer. That's not a rounding error. That's a second, dumber teacher in the room.

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What 10 Solved Classes Tell You

The confusion analysis also revealed something encouraging: 10 of the 31 classes had exactly zero errors. Underwatering, mosaic virus, boron deficiency, fungus gnats, leafhoppers, mealybugs, several others – the model has learned these perfectly on the validation set.

These classes share two things: they have visually distinctive symptoms (mosaic virus produces unmistakable leaf patterns) and their training labels are high quality (less ambiguity means less labeling disagreement). This confirms the theory: when the data is clean, the model architecture is more than capable. The bottleneck is data quality, not model capacity.

This is why I spend my time cleaning data instead of chasing bigger models. A model with 10 times more parameters trained on the same noisy data will make the same mistakes, just with more confidence. Confidently wrong is worse than uncertain.

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Why This Matters for Your Plants

You will never see any of this. You upload a photo, you get a diagnosis in milliseconds. Nobody has ever opened an app and thought “I bet they ran 47 hyperparameter experiments to calibrate this.” Nor should they.

But it's why PlantLab can tell potassium deficiency from magnesium deficiency, a distinction that experienced growers argue about in person, and that general-purpose AI tools get wrong with total confidence. It's why the accuracy number (99.1% across all 31 classes) is measured equally across every condition, not inflated by the easy ones. And it's why that number moves up instead of staying frozen at whatever the first training run produced.

Not sure what's wrong with your plant? Try the current model free at plantlab.ai. Three diagnoses a day, results in under a second.

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FAQ

How often does PlantLab retrain its models?

I run improvement cycles every few weeks. Each cycle includes a confusion analysis, data audit, and targeted label review before retraining. A full retrain takes 3-5 days of GPU time.

What's the difference between PlantLab's approach and fine-tuning a general AI model?

General-purpose vision models like GPT or Gemini were trained on billions of general images. Fine-tuning adjusts them slightly for a new task. PlantLab trains purpose-built models from scratch on 200,000+ cannabis images, using a 4-stage pipeline where each model answers one specific question. This gives us direct control over training data quality, confusion pairs, and accuracy metrics.

Why not just use a bigger model?

Bigger models don't fix noisy labels. They memorize them more effectively, which is worse. The bottleneck is data quality, not model capacity. A targeted label review that fixes 64,000 mislabeled images improves accuracy more than doubling model size. And smaller, specialized models run in 18 milliseconds instead of 2-5 seconds, which matters when you're trying to automate anything.

Can I see PlantLab's accuracy data?

I publish 99.1% balanced accuracy across 31 condition and pest classes, measured equally across all classes. I also publish where the model struggles. Potassium and magnesium confusion remains the hardest visual distinction.

Start Here

Something looks wrong. Maybe the bottom leaves are yellowing. Maybe the tips are curling. Maybe you walked into your tent and something just looked off in a way you can't articulate but your gut knows isn't right.

So you did what every grower does: you took a photo, posted it online, and got twelve different answers. Someone said CalMag. Someone said flush. Someone said “two more weeks.” None of them agreed on what the actual problem is.

This guide won't do that. It walks through a systematic process: look at where the damage is, what it looks like, and narrow it down to a specific cause. No guessing, no bro science, no “could be anything, hard to tell from the photo.”

Step 1: Where Are the Symptoms?

Look at where the damage is happening. Location tells you more than color does.

Symptom Location	Most Likely Causes
Bottom/older leaves first	Nitrogen deficiency, magnesium deficiency, potassium deficiency
Top/new growth first	Iron deficiency, calcium deficiency, light burn, heat stress
Entire plant	Overwatering, underwatering, pH lockout, root problems
Leaf surfaces (spots/patches)	Pests (spider mites, thrips), diseases (septoria, powdery mildew)
Buds/flowers	Bud rot, caterpillars, light burn
Stems/branches	Phosphorus deficiency, fusarium, root rot

Here's the rule that eliminates half the guesswork: mobile nutrients (nitrogen, magnesium, potassium, phosphorus) move from old leaves to new ones. When they run low, old growth sacrifices itself first. Immobile nutrients (iron, calcium) stay put – so deficiency shows up on new growth first.

Bottom-up damage? Mobile nutrient problem. Top-down damage? Immobile nutrient or environmental. That single distinction saves you from chasing the wrong diagnosis for a week.

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Step 2: What Do the Leaves Look Like?

Yellow Leaves

Ah, yellow leaves. The “check engine light” of cannabis growing. Universally alarming, completely nonspecific. Seven different things cause yellowing, and the forum advice for all of them is “probably CalMag.” The pattern of yellowing is what actually matters.

Yellow Pattern	Condition	How to Tell
Uniform yellowing, bottom leaves, veins included	Nitrogen deficiency	The whole leaf goes pale – veins too. Oldest leaves die first while new growth stays green. The classic.
Yellow between veins, bottom leaves, veins stay green	Magnesium deficiency	The leaf looks striped – green veins on yellow background. Often appears mid-to-late flower. This is the one where CalMag actually might be the answer.
Yellow between veins, top/new leaves, veins stay green	Iron deficiency	Identical pattern to magnesium, but on new growth instead of old. Easy to confuse the two if you're not paying attention to which leaves are affected.
Yellow leaf edges progressing inward	Potassium deficiency	Starts as yellow margins, turns brown and crispy. Sometimes mistaken for nute burn but the pattern is too consistent and progressive.
Yellow spots with brown centers	Calcium deficiency	Irregular brown/bronze splotches on newer growth in veg, but can appear on lower fan leaves during flower. Leaves may also twist or distort.
Uniform pale yellow, all over	pH lockout	Every nutrient is present in the soil. The plant just can't access any of it because pH is off. Fix pH first, wait 5 days, then reassess.
Yellow and drooping	Overwatering	The leaves feel heavy and waterlogged, not crispy and dry. The soil is still wet. You watered it because you were worried about it and now it's worse. We've all been there.

Bottom-up yellowing with veins turning yellow? That's nitrogen deficiency – the single most common issue for cannabis growers. See our complete nitrogen deficiency guide.

Yellow leaves but genuinely can't tell which deficiency? You're not alone – even experienced growers get these confused. PlantLab's AI was specifically trained to distinguish between 7 nutrient deficiencies that look nearly identical to the human eye. It's more reliable than asking strangers on Reddit, and faster than waiting three days for the wrong treatment to not work.

Brown Spots and Edges

Brown Pattern	Condition	How to Tell
Brown crispy edges, leaf margins	Potassium deficiency	Edges burn inward from the margins. Bottom leaves first. Often shows up in flower when K demand spikes.
Brown/bronze spots expanding over time	Calcium deficiency	Newer growth in veg, lower fan leaves in flower. Spots are irregular with browning edges, not perfectly round.
Brown spots with target-like pattern	Leaf septoria	Dark center ringed by lighter brown and a yellow halo – a bullseye pattern. Shape is roughly circular to irregular. Lower canopy in humid conditions.
Brown/gray mush inside buds	Bud rot (Botrytis)	The one that keeps growers up at night. Internal mold that starts inside your densest colas. By the time you see it on the outside, the inside is already gone.
Brown/rust colored bumps	Rust fungus	Raised bumps on leaf undersides, like tiny blisters. Often overlooked until it's widespread.

Curling Leaves

Curl Direction	Condition	How to Tell
Curling UP (taco-ing)	Heat stress, light stress	The plant is folding its leaves to reduce the surface area exposed to your too-close light. Top canopy affected most.
Curling DOWN (the claw)	Nitrogen toxicity	Dark green, glossy, tips hooking downward. The plant equivalent of drinking too much coffee. You overfed it.
Edges curling up	Potassium deficiency, heat	If the edges are also brown and crispy, it's K. If just curling, it's heat.
New growth twisted/distorted	Calcium deficiency	New leaves come in looking wrong – twisted, cupped, malformed. Not just curling, actually misshapen.

White or Discolored Patches

Appearance	Condition	How to Tell
White powdery coating	Powdery mildew	On fan leaves: wipes off with your finger, leaving clean green underneath. On sugar leaves near buds where trichomes are dense, the wipe test is unreliable – use a 10x loupe instead. PM looks flat and dusty; trichomes are three-dimensional with visible stalks and mushroom-shaped caps.

| White webbing between leaves | Spider mites | Fine webs between branches. Flip a leaf over – if you see tiny moving dots, you have a serious problem. | | Bleached/white tips | Light burn | Primarily on the top canopy, closest leaves to your light. Move the light up. | | Purple/red stems and undersides | Phosphorus deficiency, cold, or genetics | Three common causes: (1) genetics – many strains naturally run purple stems, (2) cold temperatures below 60F/15C trigger anthocyanin production independently of nutrition, (3) actual P deficiency, which also causes dark leaves, slow growth, and stiff/brittle foliage. If purple stems are the only symptom, it's almost certainly not phosphorus. |

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Step 3: Check for Pests

Pests leave evidence. Nutrient deficiencies create patterns. Knowing the difference matters – treating the wrong cause wastes time and can make things worse.

A jeweler's loupe is the single best diagnostic tool you can own. A 10x loupe ($8) catches most pests; a 60x pocket microscope ($15) is needed for broad mites and russet mites, which are invisible at lower magnification.

Pest	What You See	Where to Look
Spider mites	Fine webbing, tiny dots on leaves, stippling damage	Leaf undersides, near veins. By the time you see webs, the colony is already massive.

| Thrips | Silver/bronze streaks, tiny elongated insects | Upper leaf surfaces, inside new growth. The streaks are where they've been feeding. | | Aphids | Clusters of small bugs, sticky residue (honeydew) | Stems, new growth tips. They reproduce fast – a few today, hundreds next week. | | Broad mites / Russet mites | Twisted, distorted new growth; glossy or plastic-looking leaves; stunted tops | Invisible to the naked eye (need 60x+ magnification). Often misdiagnosed as heat stress, pH problems, or calcium deficiency. One of the most devastating cannabis pests because they're identified too late. | | Fungus gnats | Small flies near soil surface | Topsoil, especially in chronically overwatered pots. Adults are harmless; larvae feed on root hairs and create entry points for pathogens like Fusarium and Pythium. Dangerous for seedlings, less so for established plants unless the infestation is heavy. | | Whiteflies | Cloud of tiny white insects when plant is disturbed | Leaf undersides. Shake the plant gently – if a cloud of tiny white things takes off, you know. | | Caterpillars | Frass on/near buds, unexplained cola browning, holes in leaves | Inside buds, under leaves, along stems. Outdoor grows especially. The real threat is budworms boring into dense colas – the frass they leave behind promotes bud rot, which is often worse than the direct feeding damage. |

The key distinction: Pest damage is random and localized – wherever the pest fed. Nutrient deficiencies are systematic – they follow predictable patterns based on nutrient mobility. If the damage pattern doesn't make sense for any deficiency, get the loupe out.

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Step 4: Rule Out the Usual Suspects First

Before you diagnose a deficiency and start adjusting nutrients, check the three things that cause most of the problems most of the time. Boring advice, but it would prevent about 60% of the “what's wrong with my plant” posts on every growing forum.

pH (The Actual Answer to Most Problems)

Here's the uncomfortable truth: the majority of “deficiency” symptoms in cannabis are actually pH lockout. Every nutrient is sitting right there in the soil. The plant just can't absorb any of it because the pH is wrong.

Medium	Ideal pH Range
Soil	6.0 – 7.0
Coco coir	5.5 – 6.5
Hydro/DWC	5.5 – 6.0

Check your pH before you diagnose anything. If it's off, fix it, wait 3-5 days, then see if the symptoms are still progressing. This is less exciting than diagnosing a rare micronutrient deficiency, but it's correct far more often. “pH your water bro” is the one piece of forum advice that's right almost every time.

Watering (The Other Usual Suspect)

Symptom	Overwatering	Underwatering
Leaves	Drooping, heavy, plump	Drooping, dry, thin
Soil	Wet, slow to dry	Dry, pulling from pot edges
Recovery time	Slow (2-3 days)	Fast (hours after watering)
Pot weight	Heavy	Light

The “lift the pot” test is free and takes one second. If the pot is heavy, stop watering. If it's light, water it. More sophisticated than most diagnostic protocols, honestly.

New growers overwater because they're paying too much attention. The plant doesn't need water every day. If the soil is still moist 2 inches down, walk away. Watering your plant because you're anxious about it is the gardening equivalent of refreshing your email.

Light and Heat

• Light burn: Bleached/white leaf tips closest to light. Your light is too close. Move it up.
• Heat stress: Leaves taco upward, fox-tailing in flower. If your hand is uncomfortable at canopy height for 30 seconds, the plant is uncomfortable all day.
• Light deficiency: Stretching, thin stems, pale color. The plant is reaching for something that isn't there.

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The Cannabis Deficiency Quick-Reference Chart

For when you've checked pH, watering, and environment and the problem is still getting worse:

Nutrient	Mobile?	Where It Shows	Primary Symptom	Secondary Symptom
Nitrogen (N)	Yes	Old/bottom	Uniform yellowing	Leaves cup upward, fall off
Phosphorus (P)	Yes	Old/bottom	Dark leaves, slow growth	Purple stems (also genetics/cold)
Potassium (K)	Yes	Old/bottom	Brown crispy edges	Yellow margins
Calcium (Ca)	No	New/top (veg), lower leaves (flower)	Brown/bronze spots	Distorted new growth
Magnesium (Mg)	Yes	Old/bottom	Interveinal yellowing	Green veins on yellow leaf
Iron (Fe)	No	New/top	Interveinal yellowing	Same as Mg but on new leaves
Nitrogen tox.	-	All	Dark green, “the claw”	Tips hook down, glossy

The mobile/immobile rule is worth memorizing. It's the difference between diagnosing in 10 seconds and spending a week on GrowWeedEasy trying to match photos.

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When Eyeballing It Isn't Enough

Visual diagnosis works when symptoms are textbook. In reality, symptoms are rarely textbook. They're a blurry phone photo of a leaf under a purple blurple light, and three different conditions look identical at that resolution.

It breaks down especially when:

• Multiple problems overlap – spider mites AND potassium deficiency at the same time. Treat one, miss the other, wonder why the plant isn't recovering.
• Early symptoms are subtle – the difference between “early nitrogen deficiency” and “normal bottom leaf aging” is obvious in a textbook photo and invisible in your tent at 6 AM.
• Similar conditions need distinguishing – potassium vs magnesium deficiency requires comparing leaf position, vein color, edge pattern, and progression simultaneously. This is where “add CalMag and see what happens” comes from – it's not laziness, it's that telling the two apart with your eyes is genuinely hard.

PlantLab's AI was trained specifically on these ambiguities. It analyzes 31 cannabis conditions and can distinguish between 7 nutrient deficiencies that experienced growers regularly confuse. Not because it's smarter than a grower with 20 years of experience – but because it's been trained on 200,000+ images and doesn't get fooled by blurple lighting.

Try it free at plantlab.ai – 3 diagnoses per day, no credit card.

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FAQ

What is the most common cannabis plant problem? Nitrogen deficiency, by a wide margin. It's the most common real deficiency, and pH lockout causing symptoms that look like nitrogen deficiency is even more common. If you can only learn to identify one thing, learn what nitrogen deficiency looks like. Then learn to check your pH so you can rule out the fake version.

Why are my weed plant's leaves turning yellow? It depends. (Sorry. But it really does.) Start with where: bottom leaves = nitrogen, magnesium, or potassium. Top leaves = iron or calcium. Everywhere at once = pH lockout or root problems. The answer to “why are my leaves yellow” is always another question: “which leaves, and what does the yellowing pattern look like?” The table in Step 2 above will narrow it down.

How do I tell if my cannabis plant is overwatered or underwatered? Both cause drooping, which is unhelpful. The difference is in the leaves: overwatered leaves feel heavy, plump, and the soil is still wet. Underwatered leaves are papery thin and the plant perks up within hours of getting water. The pot-lift test works: heavy pot = too wet, light pot = too dry. Overwatering is far more common than underwatering, because new growers hover.

Can a cannabis plant have multiple problems at once? Frequently. Stressed plants attract pests, incorrect pH causes cascading lockouts across multiple nutrients, and a spider mite colony feasting on a plant that's already potassium-deficient produces a confusing mess of symptoms. Prioritize the most severe issue first. Fix that, stabilize, then address the next one. Trying to treat everything simultaneously usually means treating nothing effectively.

Should I remove yellow or damaged leaves? If a leaf is mostly brown and crispy, remove it – it's done photosynthesizing and it's just attracting pests. If it's partially yellow, leave it alone. It's still working. The plant will drop it when it's done with it. Never remove more than 20% of foliage at once, or you'll trade a nutrient deficiency for light stress from suddenly exposed lower growth.

What does it mean when my marijuana plant leaves curl up? Usually heat or light stress. The plant is doing what you'd do if someone held a heat lamp over your head – curling up to reduce its exposure. Move the light higher, improve airflow, or reduce intensity. If the curling comes with brown crispy edges, that's potassium deficiency instead. If the leaves are dark green and curling down (the claw), that's nitrogen toxicity – you overfed it.

How do I know if it's a nutrient deficiency or a pest problem? Deficiencies are systematic: they affect leaves in predictable order (old-to-new or new-to-old), create consistent patterns (interveinal, marginal, uniform), and progress gradually. Pest damage is chaotic: random holes, stippling in patches, silvery streaks where something was feeding, and actual visible bugs if you flip leaves over and look. When in doubt, get a 10x loupe and inspect the undersides. If nothing is moving and nothing is webbed, it's probably not pests.

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Detailed guides: – Nitrogen Deficiency: Complete Visual Guide – Calcium vs Magnesium Deficiency: A Visual Comparison – Potassium Deficiency: Burnt Edges and Rust Spots – How AI Diagnoses 31 Cannabis Conditions in 18ms – Nutrient Antagonism: When Adding More Makes It Worse – Why I Built PlantLab

The Short Version

Nutrient antagonism is when excess of one nutrient physically blocks another from being absorbed – your plant has enough of what it needs, it just can't get to it. Adding more of the blocked nutrient usually makes things worse. A 1953 agricultural chart called the Mulder's Chart maps all of these interference relationships; PlantLab's diagnosis now applies that same logic automatically, flagging the most likely excess nutrient in every analysis.

What this post covers: – Why “add more” is sometimes the exact wrong answer – The Mulder's Chart: what it shows and how to read it – The four antagonism traps cannabis growers hit most often – How to tell antagonism from a true deficiency – What to actually do once you've identified the likely excess

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You're three weeks into flower. New growth is showing interveinal chlorosis – yellowing between the veins. Classic iron deficiency. You've seen it before. You adjust your pH, add some chelated iron, wait a few days. Nothing. You add more. The leaves get worse. Two weeks of this and your runoff EC looks completely normal. What is going on?!

Here's the thing: your plant probably has plenty of iron. The problem is that something else is blocking it from getting in. You're chasing a deficiency that isn't really there.

That's nutrient antagonism – and it's the reason “add more nutes” is sometimes the exact wrong answer.

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Why Adding More Makes It Worse

Plant roots absorb nutrients as ions through specific transport proteins – essentially tiny gates in the root membrane, each specific to particular ions. The problem is that some ions are chemically similar enough to compete for the same gate.

Calcium (Ca²⁺) and magnesium (Mg²⁺) compete directly. Iron (Fe²⁺) and manganese (Mn²⁺) compete. Phosphate and zinc compete. When one is present in excess, it floods the available uptake pathways and crowds out the competition. The blocked nutrient can be sitting right there in your solution at perfectly normal levels – the plant just can't get it through.

Think of it like a packed venue with a capacity limit. Having more people waiting outside doesn't get anyone in if the wrong crowd already filled the room.

Your nutrient solution looks fine on paper. Your runoff TDS is normal. But the plant is effectively starved of the blocked nutrient. When you add more of the blocked nutrient, you're just adding more people to the outside line.

The fix is not to add more of the blocked nutrient – it's to reduce the excess that's doing the blocking.

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The Mulder's Chart: 70+ Years of This Being a Known Problem

In 1953, agricultural researcher Cornelis Mulder published an analysis of how essential plant nutrients interfere with each other. The interactions he mapped have been confirmed and refined by plant scientists ever since. Farmers and agronomists have used the resulting chart as a standard diagnostic tool for decades.

Most cannabis growers have never heard of it.

Here's the cannabis-relevant subset, derived from the interaction patterns that show up most in real-world growing:

If you have excess of...	It blocks uptake of...
Calcium	Boron, Iron, Magnesium, Manganese, Phosphorus, Potassium, Sulfur, Zinc
Phosphorus	Copper, Iron, Manganese, Zinc
Potassium	Boron, Calcium, Magnesium, Nitrogen, Phosphate
Nitrogen (toxicity only)*	Boron, Copper, Potassium
Zinc	Copper, Iron, Manganese
Iron	Manganese
Manganese	Iron

Calcium is the overachiever – it can block uptake of nearly everything else. This is also why CalMag can backfire when calcium is already the problem.

One important distinction about nitrogen: it's the most visually distinctive excess in this table. While every other entry can only be inferred from the deficiencies it creates, nitrogen toxicity has its own unmistakable signature – dark green, shiny leaves with tips curling downward, the “claw.” You can't look at a plant and spot phosphorus excess; you see iron and zinc and manganese deficiency appearing together, and you work backwards from that.

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The Four Traps Cannabis Growers Hit Most

The following are the scenarios you'll recognize from forum threads, grow journals, and that one grow that made you seriously question everything.

Trap 1: The Calcium-Magnesium See-Saw

Magnesium deficiency and calcium deficiency share visual overlap in early stages – interveinal chlorosis, lower leaves first. The forum answer is CalMag. CalMag works great when both calcium and magnesium are actually depleted. The problem: if calcium is already elevated (hard tap water, previous CalMag doses stacking, heavy base feed), adding more calcium deepens the antagonism against magnesium uptake. The magnesium deficiency gets worse, you add more CalMag, calcium climbs higher, and you're doing laps around the same problem.

The lesson isn't that CalMag is bad. It's that the same supplement that fixes a true Ca/Mg deficiency actively worsens a calcium-driven lockout.

Trap 2: The Phosphorus Stack

Late veg or early flower: you flush the plant, then hit it with a P-heavy bloom formula to push the transition. Phosphorus accumulates fast. Now iron, zinc, and manganese uptake are all suppressed simultaneously. Three separate deficiency symptoms appearing in the same growth period, on new leaves, none of them responding to treatment – that pattern almost always points to one excess, not three separate missing nutrients.

Trap 3: The Hard Water Baseline

Tap water at 200+ ppm calcium (common in many regions) puts you at or above the antagonism threshold before you've added a single ml of nutrients. Your nutrient line looks balanced on the label, but calcium is already dominating the root zone before the grow starts. By mid-flower, everything from magnesium to iron to zinc is struggling. Check your source water with a TDS meter before spending two weeks blaming your feeding schedule.

Trap 4: Soil Buildup

Amended soil and organic media are forgiving by design – but they accumulate phosphorus and potassium over time. By week 6 or 7 of flower, even a conservative feed is compounding on top of weeks of residual buildup. Micronutrient lockout becomes predictable. If your soil grows have historically gone sideways in late flower despite clean feeding, this is the first thing to look at.

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Antagonism or True Deficiency: How to Tell

If the problem is antagonism, adding more of the deficient nutrient won't fix it – and may accelerate the decline. Use these signals to read the situation:

Signs you're probably dealing with antagonism: – You already added the deficient nutrient and symptoms didn't improve or got worse (i.e. stop, just stop!) – The symptom pattern matches a known antagonism pair from the table above (i.e. hey, that's it) – Multiple micronutrient deficiencies appeared at the same time, not sequentially (i.e. why does this happen to me) – Runoff EC/TDS is elevated (i.e. I knew it!) – You've been stacking the same formula for several weeks without a flush or reset (i.e. it worked before, what gives?)

Signs it's probably a true deficiency: – Fresh grow, new medium, feeding hasn't been aggressive – Runoff EC is low – Single deficiency symptom, nothing else overlapping – Deficiency appeared early in the cycle when soil is still fresh

The diagnostic question to ask yourself: “I'm seeing [symptom]. What have I been feeding heavily that might be blocking this?” If the Mulder's table shows a plausible answer, that's your starting point.

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What to Do When You Suspect Antagonism

1. Stop adding the blocked nutrient. More iron won't fix iron lockout.
2. Identify the likely excess using the table above. What have you been stacking?
3. Reduce that input. In DWC, replace the reservoir and mix fresh at lower concentration. In coco, back off feed strength and let runoff EC drop. In soil, a water-only cycle at half strength is usually enough to start clearing it.
4. Let runoff EC normalize before resuming full feeding.
5. Reintroduce the blocked nutrient at a conservative dose once the excess has cleared.

Don't automatically reach for a full aggressive flush. Antagonism builds gradually and usually unwinds gradually too. You're correcting a balance issue, not nuking the root zone – unless the situation is severe, ease into it.

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How PlantLab Applies Mulder's Chart

PlantLab's /diagnose endpoint detects individual nutrient deficiencies from photos. When we built the nutrient subclassifier, the hardest cases weren't single deficiencies – they were patterns where three or four micronutrients were suppressed simultaneously. Those cases almost always had a single excess driving them, not three separate missing nutrients.

So we encoded the Mulder's Chart antagonism graph directly into the analysis layer. Every diagnosis now returns a mulders_hypotheses field: a ranked list of the most likely excess-nutrient candidates, inferred from the co-occurring deficiency pattern.

Here's what that looks like in practice. If the model detects iron deficiency + zinc deficiency + manganese deficiency in the same image:

{
  "mulders_hypotheses": [
    {
      "excess": "phosphorus_excess",
      "explains": ["iron_deficiency", "zinc_deficiency", "manganese_deficiency"],
      "evidence": 2.38,
      "evidence_count": 3
    },
    {
      "excess": "calcium_excess",
      "explains": ["iron_deficiency", "zinc_deficiency", "manganese_deficiency"],
      "evidence": 2.38,
      "evidence_count": 3
    },
    {
      "excess": "zinc_excess",
      "explains": ["iron_deficiency", "manganese_deficiency"],
      "evidence": 1.61,
      "evidence_count": 2
    }
  ]
}

Example reflects multi-label nutrient detection shipping in the next release.

The evidence score is the sum of confidence scores for each condition the excess would explain. Phosphorus and calcium both explain all three deficiencies equally in this example – both rank first. Zinc excess only explains two of the three, so it ranks lower.

This is a hypothesis, not a measurement. The model doesn't have access to your water report, feeding history, or what's in your soil. What it gives you is a starting point: here's which excess is most consistent with the visual pattern. For most growers, that's the hardest part of the diagnosis loop – not knowing which direction to even look.

For growers running API-driven automation – Home Assistant, Node-RED, custom controllers – the mulders_hypotheses field maps directly to an action: phosphorus_excess → reduce P in next feed. The diagnosis points you to the problem; your setup handles the correction.

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Limitations

This is inference, not measurement. A soil test or water analysis gives you actual numbers. mulders_hypotheses gives you ranked hypotheses based on what the plant looks like. Use it to direct your investigation, not replace it.

pH is a parallel problem. Deficiency symptoms caused by pH outside the optimal range look identical to antagonism symptoms. If your runoff pH is off, fix that first – the Mulder's table won't help you here.

Rare interactions have less training data. Copper deficiency is a newer addition to the model; detection confidence is lower than for iron or nitrogen. Treat copper-related hypotheses with more skepticism until more data accumulates.

Visual overlap is real. Late-stage symptoms, nitrogen toxicity masking other issues, and grow-stage-specific presentation can confuse any visual analysis. If a diagnosis doesn't match what you're seeing, trust your eyes and reach for a test kit.

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Related Reading

• Nitrogen Deficiency in Cannabis – the most common deficiency, and how to tell it apart from light deficiency and senescence
• Calcium vs Magnesium Deficiency: A Visual Comparison – the classic confusion pair, and why reaching for CalMag can deepen a lockout
• Potassium Deficiency: Burnt Edges and Rust Spots – the deficiency most often mistaken for nutrient burn, and how excess Ca/Mg locks it out
• What's Wrong With My Cannabis Plant? – the full visual diagnosis guide when the symptoms do not fit one clean cause

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Try PlantLab free at plantlab.ai. Create a free account and get 3 diagnoses per day — upload a photo and the diagnosis returns structured JSON: conditions detected, confidence scores, and the mulders_hypotheses field for any nutrient patterns that fit the antagonism graph.

The short version

Most plant diagnosis tools give you a paragraph to read. PlantLab gives your automation system something to act on.

The model covers 31 cannabis conditions and pests at 99.1% balanced accuracy. Balanced means every class counts equally – a system that nails common deficiencies but misses rare pests does not score well. The output is structured JSON that Home Assistant, Node-RED, or a custom controller can read and act on without a person in the loop.

Why generic AI fails

The first time I tried AI for plant diagnosis, I uploaded a photo to ChatGPT. It told me I had a calcium deficiency. It was light burn. The two look nothing alike if you know what you are looking at, but ChatGPT was never trained specifically on plant images. It is a convincing generalist, and when it does not know, it guesses.

That is what most “AI plant diagnosis” apps actually do. Wrap a general-purpose language model, send your photo with a prompt, return whatever comes back. The output is confident, plausible, and sometimes wrong, and a new grower has no easy way to tell which time is which. It is also something you can do yourself for free, which makes paying for the service hard to justify.

The deeper problem is that even when these tools are right, they hand you prose. Useful for a person reading a screen. Useless for an automation system that needs to decide whether to adjust pH, run a fan, or send you an alert.

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What PlantLab detects

The model covers 31 cannabis conditions and pests across four families.

Nutrient issues: nitrogen, phosphorus, potassium, calcium, magnesium, iron, boron, manganese, and zinc deficiencies, plus nitrogen toxicity.

Diseases: powdery mildew, bud rot, root rot, pythium, rust fungi, septoria, and mosaic virus.

Pests: spider mites, thrips, aphids, whiteflies, fungus gnats, caterpillars, leafhoppers, leaf miners, and mealybugs.

Environmental: light burn, light deficiency, heat stress, overwatering, and underwatering.

Every class scores above 95% detection accuracy, including the rarer ones.

What you get back

{
  "request_id": "550e8400-e29b-41d4-a716-446655440000",
  "schema_version": "2.0.0",
  "success": true,
  "is_cannabis": true,
  "is_healthy": false,
  "growth_stage": "flowering",
  "conditions": [
    { "class_id": "bud_rot", "confidence": 0.92 }
  ],
  "pests": [],
  "reliability_score": 0.88
}

Not a paragraph for a person to read and interpret. A machine-readable signal. Your controller sees 92% confidence on bud rot in a flowering plant and can ramp airflow, send an alert, or log the event – keeping you informed without forcing you to step in every time.

reliability_score is a separate trust signal on top of per-class confidence. It estimates whether the entire diagnosis holds up on this specific image, which is most useful on the hard cases – mixed symptoms, lookalike conditions, edge-case growth stages. There is more on it in How PlantLab Knows When It Might Be Wrong.

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What's new in this release

The previous version of the model covered 24 conditions. This release brings it to 31. The additions came from what growers actually run into and ask about.

Bud rot is one of the worst things that can happen during flowering. Dense colas plus humid air invite Botrytis, and by the time you can see it with the naked eye, it has often already spread.

Heat stress causes leaf curling, foxtailing, and bleaching that new growers often confuse with nutrient issues. Splitting it into its own class prevents the misdiagnosis.

Fungus gnats are usually the first pest a new indoor grower meets. Caterpillars, leafhoppers, and leaf miners are common outdoor threats. Mealybugs are less common but brutal once they take hold. All five now have dedicated detection.

Boron, manganese, and zinc deficiencies fill out the micronutrient coverage. Less common than the macros, but harder to spot by eye because their symptoms overlap with other conditions.

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A diagnosis that surprised me

I sent a sample of recent images through the live service to spot-check it against my own intuition.

One result stood out. The photo was a plant that looked underwatered – drooping, leaves curling, the classic signs. The model called it overwatered. I was ready to write that off as wrong, then I went back through earlier photos. The plant had been chronically overwatered for weeks. That ongoing stress had caused nutrient lockout, which then progressed into something that looked like underwatering. The model caught the underlying cause. Without that, I would have treated the symptom and made the problem worse.

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What's next

A few things in the queue.

Multiple concurrent conditions in one image. Plants can have spider mites and a calcium deficiency at the same time. Today the API returns the primary diagnosis. Multi-label output is on the way.

Step-by-step automation guides. Home Assistant, Node-RED, and others – walkthroughs for wiring PlantLab into the stack you already run.

More real-world data. Photos from real tents, at real angles, in real lighting, sharpen the model on the conditions it actually sees – not just the clean reference shots.

PlantLab is free to try at plantlab.ai. The API returns structured JSON for every diagnosis – plug it into your automation stack and let your grow room see for itself.

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Related reading: – Why I Built PlantLab – The origin story – How PlantLab Knows When It Might Be Wrong – The reliability_score field and schema 2.0 – Nitrogen Deficiency in Cannabis: A Visual Guide – Detailed guide for the most common deficiency – API Documentation

Forest grows from the neglected seed The lands collapse, the skys cry Debts settle between you and me It's not supposed to be my fault Why you have to make me die?

A press release landed in my inbox this week about the Marine Institute's research vessel Tom Crean. It was full of the expected details. Days at sea. Scientific surveys. Fisheries research. Seabed mapping. Climate studies. The vessel travelled more than 31,000 nautical miles last year and is now one of Ireland's most important scientific assets.

But it was not the statistics that caught my attention. It was the name.

Years ago, when I wrote about Tom Crean, there was often a need to explain who he was. He was known in parts of Kerry and among Antarctic enthusiasts, but he had not yet become a household figure. Mention his name and many people would look puzzled. Back then, from time to time I would stop by Tom Crean's pub in Annascaul. Visitors liked to joke that they had made it to the South Pole, albeit by a somewhat easier route than Crean himself. Today the pub is a destination on the tourist map. Then, it felt more like a quiet local reminder of a largely forgotten man.

Now, things are different. A state-of-the-art research ship carries his name. Schoolchildren learn about him. His story has entered the fabric of Irish public life. The remarkable thing is not that Tom Crean has changed. The remarkable thing is that we have.

Names are curious things. We tend not to notice them until we stop and ask why they are there. Every nation reveals something about itself through the names it gives to ships, bridges, buildings and institutions.

The choice of Tom Crean is an interesting one. He was not a politician. He was not a military leader. He left no manifesto behind him and founded no movement. By most conventional measures, he was an unlikely candidate for national recognition.

What he possessed was something quieter. Endurance. Reliability. Courage. A willingness to keep going when circumstances became difficult. In the great Antarctic expeditions of the early twentieth century, he was rarely the man making the speeches or standing in the spotlight. More often he was the man pulling the sledge.

Perhaps that is precisely why he speaks to modern Ireland. There was a time when Irish history concentrated heavily on conflict and politics. Increasingly, however, we seem drawn to different kinds of figures. People who built things. People who explored. People who persisted. People whose achievements emerged not from ideology but from character.

The research vessel Tom Crean seems an almost perfect symbol of that shift. The original Tom Crean ventured towards the unknown edges of the map. The modern Tom Crean explores a different frontier. It studies the oceans around us, gathers evidence, maps the seabed and helps us understand our place in a changing world. Both are engaged in exploration. One travelled into physical darkness. The other seeks to illuminate what lies beneath familiar waters.

I found myself smiling at the thought. Somewhere between the quiet pub in Annascaul and the research vessel sailing out of Galway Harbour lies a story about Ireland itself. Not about how Tom Crean became famous. About how we finally learned to recognise him.

David Marshall Costa Coffee Omni Park Shopping Centre Swords, Co. Dublin

You've probably run into some Obsidian evangelist espousing some convoluted system of organizing all their notes, and building some knowledge base. Heck, I was just involved in a conversation about Personal Knowledge Management. This isn't that article.

My personal use of Obsidian tends to be simple. I build into it the tools that I need to get things done, to make things easier. There is a saying (which I can't locate, so I'll paraphrase) that a system should only be as complicated as needed, and nothing more. The idea is there is no reason to build a Rube Goldberg machine when all you really need is a wrench. That's the approach I take: use a wrench to bolt on a few tools, and tighten things up where necessary.

So, this article will walk through how I work with things now. This system has evolved a bit over the last few weeks, and is likely to evolve further. But the primary idea is, and will remain: keep it simple, but make it useful.

Planning

When I switched from WordPress to write.as, the biggest thing I “lost” was having a way to plan the articles that I wanted to work on. Obsidian recently added a feature called Bases that made it simple to set up a table containing all the articles for all three of my sites:

View of the main planning table. Unpublished articles blurred—no spoilers! ;)

The simplicity here is that the table quickly lets me see which articles I have published, which are in the planning / drafting stage, and the scheduled date for the articles. But, looking at a table this big could be tricky to navigate. Fortunately, I have a couple of alternate views available:

The Plan / Draft view lets me see just the articles I am working on.

And the Published view lets me see what I've already posted.

There are two “genius” parts to this system. First, all this information is part of the article, it's not some extra information that I need to keep track of. Second, I can update the “Published”, “Scheduled” and “Status” fields from this list without having to open the specific post. How is this done?

New Post Drafts

Every time I have a new idea, I create a new document in the website's folder, and insert a template:

Blog Template is for website posts, CMP Template is for my archived podcast posts.

Once the Blog Template is inserted, the document shows up in the Planning table. After the template is inserted the document looks like this:

Template inserted in document, with Properties for the Planning table circled.

You might notice there is both an Untitled field, and a Title field. Untitled is Obsidian's filename for the document, whereas Title is the actual article title. Why both? Obsidian places some restrictions on the names of documents. For example, the character : isn't allowed in an Obsidian filename, but there are times when I need it in an article title.

From here, writing is dead simple…

Writing In Obsidian

The full writing environment, with a few highlighted items.

Now it's just time to write. The majority of the screen is dedicated to the editing window where I can work on an article. If I have images (like this article) they can be drag-n-dropped into place. To the left is the Navigation panel, with my folders on the top half, and the files in the selected folder on the bottom. To the left of the editing window is the version control panel for the current article (I'll explain my choice in the next section).

When I am done with the article I export it to an output directory. The export process converts any Obsidian specific markup (such as using wiki style links) to standard Markdown formatting, and places all the attachments (images) in a folder with the article. This makes it dead simple to upload to write.as.

Obsidian Plugins

I have a selection of Obsidian community plugins that I use for working in Obsidian as can be seen in this image:

Image: screenshot of installed community plugins.

The most important of these are Harper and LanguageTool, both of applications these plugins call are installed locally on my system as I have a slow internet connection, and prefer to have my tools running locally. markdown export is the export tool I use for blog posts. Notebook Navigator enhances Obsidian's built-in navigation panel a ton, I've barely scratched the surface of what it can do. Outliner provides better list handling, allowing drag-n-drop of items, folding, and more. Typewriter Mode is really only used for one feature: highlighting the current line I am editing. (I have been doing this for 40+ years as it helps with my unique form of dyslexia.)

Remotely Save is the tool I use for syncing my vaults between systems. Version Control is a simple built-in version control system. The advantage to using it over git or another external version control system is that the versions of my documents are synced with Remotely Save. This removes the need to synchronize a git repository separately from the vault.

Zotero Integration allows me to search and insert bibliographic references when I need them. Alas, write.as doesn't support reference links, so I have to do it inline, instead of in end-note style.

Safi Site Audit is a tool that is unique to this vault for me. It's used to check the health of my sites, and catch any major issues, like broken links, missing alt text, and so on. I don't run it often, but it's good to be able to check and generate a report once in a while.

Conclusion

Obsidian, between it's recently added Base feature and a handful of plugins makes for a great writing environment for me. The ability to track the status of the posts I am working on, have version control over the documents, and have some additional tools that allow me to customize the environment to my liking.

I know there are a lot of evangelists of Obsidian out there, and their enthusiasm and overly complicated setups can make Obsidian seem unapproachable. Personally, I've found that it is excellent if you tailor it to your specific needs. And that is what is most important, and makes me most productive with a tool: doing what I require in the way that I need.

This wasn't too detailed in terms how I actually tweak things (like key bindings for certain tools). Hopefully it gives you some insight into my workflow. And maybe it will give you some ideas about how you can refine your workflow.

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Categories: #HowTo Tags: #obsidian, #tool, #writing, #blogging, #configuration, #workflow License: Copyright Unattributed. Licensed under Creative Commons BY-NC-SA 4.0.

Caution: continued from yesterday’s post about switching from one markdown editor to another. If that sounds boring to you, read no further.

To recap a little: Yesterday I wrote about how much I like a side by side view option in a markdown editor. The left pane shows what you’re writing. The right pane shows what it looks like with markdown applied. IA Writer does this very well for me, but I have issues with it being a kind of one-way sync between my different platform devices. So, I decided to try out StandardNotes.

If you’re a privacy or security nerd, you probably already know about Proton and the products that company offers, and I suspect that’s who this product is for most of all.

The value in StandardNotes, for me, is the side by side view option which IA Writer also has, but with terrific cross-platform sync between (probably) anything without having to jump through hoops or do much toggling when setting it up. The other features are a distant second in my case. The question was do I care about that enough to pay $90-$120 a year for it? Well, no. I suspect it’s priced this way because of the super duper encryption, but, that’s not a feature I was looking for in a markdown editor. With the seemingly world wide movement towards fascism and a surveillance state, I totally see the value in that. Like, it’s a good product and I’m glad I checked it out. I just don’t see myself using it enough right now.

-Ira

While preparing the first :Policy Briefings” for Marshall on Policy, I found myself stopped by “the circular economy”. Not because it is the loudest issue in Irish politics. It is not. Not because I had set out to make it the centre of anything. I had not.

But because, once I began reading around it, it started to look less like a technical environmental topic and more like a test of whether Ireland’s future can survive contact with the material world.

That phrase, “the material world”, is doing work here. The circular economy brings into view the physical conditions of national ambition: products, waste, repair, reuse, energy, extraction, transport, housing, industry, land, supply chains, households, cost, and labour. It asks what happens to all the stuff.

That sounds modest. It is not. Ireland and Europe talk constantly about transformation: green transition, digital transition, industrial strategy, resilience, competitiveness, social fairness, climate action. But circularity sits beneath much of that language and asks a stubborn question. Can these ambitions be made real in the systems by which we design, produce, consume, maintain, discard, recover, and remake?

This may be why the circular economy has begun to feel like a signal rather than a subject. It is not just one policy area among others. It may be a way of seeing whether the larger policy language is serious.

For Marshall on Policy, this discovery may open into a longer sequence provisionally titled “Transforming Ireland”.

The working question would be: what would it mean for Ireland to become capable of its own stated future?

I am not yet answering that here. This is only a note of discovery. But the circular economy may be the first clue. Transformation is not merely aspiration. It is redesign. It is not only what a country says about the future. It is what it does with the material conditions of living. For now, that is the signal.

Working source trail I am keeping a separate source note on circular economy material, including EU, Irish, political-family, research, investment, and civil society sources. The early pattern is clear enough to record: circularity is being treated not only as waste policy, but as part of competitiveness, resilience, climate action, resource security, and social wellbeing.

Working thesis: circularity as the material test of Ireland’s future

The circular economy is not merely a waste policy or an environmental adjustment. Across EU, Irish, institutional, party-political, civil society, research, and investment sources, it appears as a structural test of whether climate ambition, industrial competitiveness, resource security, consumer protection, and social fairness can be made to work together in the material world.

The emerging thesis for Marshall on Policy is that circularity asks whether Ireland’s future can survive contact with matter: products, waste, repair, design, supply chains, labour, infrastructure, cost, and the limits of extraction.

Module 1: EU institutional framework European Commission: Circular Economy Action Plan and current circular economy strategy

The European Commission frames circularity as part of the EU’s transition to “a cleaner and more competitive Europe”, with products and materials kept in circulation for as long as possible and waste and resource use minimised. It links the circular economy to reducing pressure on natural resources, halting biodiversity loss, achieving climate neutrality by 2050, and building a more resilient and competitive Europe.

The Commission also says the planned Circular Economy Act is intended to establish a Single Market for secondary raw materials, increase the supply of high-quality recycled materials, and stimulate demand for those materials within the EU.

Use in the essay: This supports the claim that circularity is now central EU policy architecture, not a peripheral green concern.

EUR-Lex: official 2020 Circular Economy Action Plan

The official Commission communication states that scaling up the circular economy will contribute to climate neutrality by 2050, decouple economic growth from resource use, ensure long-term EU competitiveness, and “leave no one behind”. It also names product design, consumer and public buyer empowerment, production processes, key product value chains, waste policy, secondary raw materials, and waste exports as parts of the policy field. [EUR-Lex]

Use in the essay: This is the formal source for circularity as a whole-economy framework rather than a recycling programme.

European Parliament Research Service: Circular Economy Act briefing

The European Parliament Research Service frames the circular economy as a move away from the linear “take-make-use-dispose” model, with two goals: lowering environmental pressures and increasing economic resilience by reducing reliance on virgin materials and unstable global supply chains. It identifies major policy issues including material security, single market fragmentation, resource-use targets, and the price gap between virgin and recycled materials

Use in the essay: This is very useful for the “quiet test” argument because it names the structural tensions: security, markets, measurement, cost, and resilience.

Council of the EU: conclusions on a climate-resilient and circular Europe

The Council approved conclusions on “Europe’s Environment 2030”, stressing the urgent need to accelerate action toward EU environmental and climate goals, especially climate adaptation and the circular economy. It also says the circular economy is one of the major challenges facing the EU and refers to the need for a stronger market for secondary materials, increased oversight of online platforms, and protection from unfair competition from third countries.

Use in the essay: This source helps show that circularity has moved into the language of resilience, security, markets, and competitiveness.

Module 2: Ireland-specific sources

?Government of Ireland: Whole of Government Circular Economy Strategy 2026-2028

Ireland’s 2026-2028 strategy defines the circular economy as minimising waste and resource use while keeping the value of products and materials for as long as possible. It says circularity can reduce dependency on primary resource extraction and complex global supply chains, strengthening resilience and competitiveness in the face of supply shocks. [gov.ie]

The strategy positions circularity as central to Ireland’s economic competitiveness, environmental sustainability, and social wellbeing, and includes an objective to increase Ireland’s circular material use rate by 2 percentage points each year, aiming for 12% by 2030. [gov.ie]

Use in the essay: This is the core Irish policy source. It directly supports my claim that circularity sits where climate, industry, and fairness meet.

Government of Ireland: circular economy policy page

The Government’s circular economy page says achieving the full benefits of circularity requires more than individual action: it requires government policy, regulation, new business models, and new systems of production. It also says the Circular Economy and Miscellaneous Provisions Act 2022 defines the circular economy for the first time in Irish domestic law and provides a legal basis for government action. [gov.ie]

Use in the essay: This gives me a strong source for saying that Ireland itself recognises circularity as systemic, not merely behavioural.

EPA: Circular Economy Programme 2021-2027

The EPA describes Ireland’s Circular Economy Programme 2021-2027 as the driving force for Ireland’s move to a circular economy. Its vision is an Ireland where everyone uses fewer resources and prevents waste in order to achieve sustainable economic growth. [Environmental Protection Agency]

The EPA also says the circular economy goes beyond waste management, focusing on reducing raw material use and maximising the value of materials along the production and consumption chain. [Environmental Protection Agency]

Use in the essay: Useful for the practical policy baseline and for distinguishing circularity from ordinary waste policy.

Circularity Gap Report Ireland

The Circularity Gap Report gives Ireland a circularity metric of 2.7% and says Ireland can reduce material consumption and carbon emissions by around one-third while more than tripling its circularity metric by applying circular strategies across major sectors. [dashboard.circularity-gap.world]

Use in the essay: This is the sharp diagnostic source. It gives the material basis for saying Ireland’s future vision is not yet matched by circular practice.

Module 3: European political families and parliamentary groupings

PES: Party of European Socialists

The PES 2024 manifesto commits to “a new Green and Social Deal” with clean, secure, and affordable energy, new quality jobs, and a green, carbon-free circular economy. [The Party of European Socialists]

A PES circular economy publication says Europe’s economy is based on a “take-make-consume-dispose” model and sets out PES proposals for a circular economy that would benefit workers and citizens, create a stronger and more sustainable economy, and respect planetary boundaries. [publications.pes.eu]

Use in the essay: PES helps ground the social-democratic version of circularity: just transition, workers, citizens, jobs, and planetary limits.

EPP: European People’s Party

The EPP’s circular economy resolution links circularity to the social market economy, resource efficiency, competitiveness, secondary recycled raw materials, circular business models, jobs, and reduced dependence on imported raw materials and energy. It favours a realistic, market-oriented package that simplifies regulation and supports growth. [EPP – European People's Party]

The EPP 2024 manifesto also frames environmental and climate leadership alongside economic prosperity, food security, less bureaucracy, innovation, infrastructure, and digital technologies. [EPP – European People's Party]

Use in the essay: EPP provides the centre-right circularity frame: competitiveness, efficiency, market mechanisms, industry, and regulatory simplification.

European Greens / Greens-EFA

The European Greens describe the current model as a throw-away economy based on extraction, short product lives, landfill, incineration, and pollution. Their circular economy resolution calls for resource consumption reduction, robust indicators, binding targets, the polluter-pays principle, and shifting taxation from labour to virgin resources. [European Greens]

Greens-EFA’s input on the Circular Economy Act defines circularity as minimising consumption and waste to respect planetary boundaries, keeping materials in closed and clean loops, designing sustainable and non-toxic products, extending use, repairing, reusing, and recycling. [The Greens/EFA in the European Parliament]

*Use in the essay(: The Greens sharpen the conceptual tension: circularity must mean resource reduction and product redesign, not merely better waste handling.

ALDE / liberal family / Renew-adjacent sources

The ALDE 2024 manifesto says Europe should invest in skills, digital and sustainable technologies, create jobs, reduce dependencies, and decouple growth from natural resource use and negative climate and health impacts. [WECF]

Use in the essay: This supports the liberal-centrist frame: innovation, skills, sustainable technologies, economic opportunity, and decoupling growth from resource use. I could still add a more directly circular Renew Europe source later if I want the party-family comparison to be complete.

The Left

The Left frames circularity as requiring systemic change, a paradigm shift, local authority involvement, social movements, grassroots ownership, waste reduction, reuse, collection, recycling, and repair. It also supported more ambitious recycling and landfill targets in the Parliament context described. [left.eu]

Use in the essay: The Left gives the democratic and grassroots version: circularity as social ownership, local action, and production-process change.

ECR and other right-populist or conservative sources

I found an ECR manifesto source, but the retrieved material did not give a clear circular-economy position in the available snippet. It should be parked for later manual review rather than treated as evidence of a circular-economy stance. [Europaportalen]

Use in theessay: Add only after checking the text directly. Do not overstate the position until the circular-economy content is confirmed.

Module 4: Local, regional, and civil society institutions

European Economic and Social Committee / European Circular Economy Stakeholder Platform

The EESC says the transition to a circular economy is high on its agenda as a response to the climate crisis and as an opportunity to increase European industrial competitiveness, promote sustainable economic growth, and generate new jobs. It describes the European Circular Economy Stakeholder Platform as a joint EESC and Commission initiative established in 2017 to bring together Europe’s circular economy community. [European Economic and Social ]

Use in the essay: This source is useful for the “social partnership” and civil society dimension: business, unions, NGOs, academia, youth organisations, and local initiatives.

Circular Cities and Regions Initiative The Circular Cities and Regions Initiative says it was launched and funded by the EU as part of the Circular Economy Action Plan and focuses on implementing circularity across Europe’s cities and regions. It says cities and regions are well placed to drive the transition and that the initiative offers knowledge sharing, technical support, and financial support. [Circular Cities and Regions Initiative]

Use in the essay: This is a route into Irish local government, regional development, and place-based circularity.

OECD: circular economy in EU cities and regions

The OECD says the EU has shown strong commitment to circularity at national, regional, and local level, but most economies remain predominantly linear. Its report analyses circular economy practices, challenges, and opportunities in cities and regions, based on a survey of 64 EU cities and regions, 10 place-based policy dialogues, and desk research. [OECD]

Use in the essay: This supports the idea that circularity is not only a national industrial policy but also a territorial and governance problem.

Module 5: Research, measurement, and investment

European Environment Agency

The EEA says EU production and consumption systems still rely heavily on raw materials that are not reused or recycled, creating waste, demand for virgin materials, and unsustainable consumption patterns. It says moving to a circular economy means moving away from linear production and consumption models toward systems in which products and materials are used longer and made into new products after use. [European Environment Agency]

The EEA also says that without a circular economy, Europe cannot achieve sustainability, and notes that doubling the circular material use rate by 2030 will be very challenging given recent trends. [European Environment Agency]

Use in the essay: This is the sober evidence base for saying that circularity is necessary but difficult.

European Investment Bank / European Commission investment gap report

A joint EIB and European Commission report says annual circular economy investments have reached around €120 billion, largely driven by the private sector, but identifies a remaining investment gap of around €82 billion per year between 2025 and 2040. It says the largest gaps are in circular design, end-of-life infrastructure, construction, batteries and vehicles, and textiles. [European Investment Bank]

Use in the essay: This is crucial for the delivery tension: circularity requires investment, not just aspiration.

Ellen MacArthur Foundation

The Ellen MacArthur Foundation says the circular economy is a systems solution framework for climate change, biodiversity loss, waste, and pollution, based on eliminating waste and pollution, circulating products and materials at their highest value, and regenerating nature. [Ellen MacArthur Foundation]

On the EU Circular Economy Act, it argues for a true EU single market where circular products and secondary materials can move freely, price and demand signals that make upstream circular solutions accessible and affordable, and treating the circular economy as core industrial strategy. [Ellen MacArthur Foundation]

Use in the essay: This source is useful for conceptual clarity and for connecting circularity to industrial strategy.

Zero Waste Europe

Zero Waste Europe says the Circular Economy Act should guide how Europe consumes and produces differently, empower communities, and build resilient economies through circular-sector job creation. It identifies challenges including policies that prioritise efficiency without addressing rising consumption, secondary raw materials being more expensive than primary materials, limited success of Extended Producer Responsibility in improving product design and waste reduction, and high residual waste volumes being landfilled and incinerated. [Zero Waste Europe]

Use in the essay: This gives you a campaigning and critical-policy source for the risk that circularity becomes too weak if it does not confront consumption, pricing, producer responsibility, and incineration.

Module 6: Working synthesis for Marshall on Policy

Across these sources, four broad readings of circularity emerge:

1. Institutional EU reading: circularity is part of climate neutrality, competitiveness, resilience, and the single market for secondary raw materials. [Environment]

2. Irish state reading: circularity is central to competitiveness, sustainability, social wellbeing, resource productivity, and reducing dependency on virgin materials. [gov.ie]

3. Political-family reading: different European political families converge on circularity but emphasise different routes: social justice, markets, resource limits, innovation, grassroots ownership, and competitiveness. [The Party of European Socialists]

4. Research and delivery reading: circularity is necessary but hard, requiring measurement, investment, territorial implementation, business model change, and changes in product design and consumption. [European Environment Agency]

Possible thesis sentence: The circular economy is where Ireland’s future stops being a vision and becomes a material test: can the country align climate ambition, industrial resilience, social fairness, and the physical realities of production, consumption, waste, repair, and reuse?

Add-next placeholders

Keep adding sources under these headings:

•Irish political parties: manifestos, parliamentary questions, Oireachtas debates, committee reports.

•Irish business and industry: IBEC, Chambers Ireland, construction, retail, ICT, textiles, waste and recycling sectors.

•Trade unions and labour: just transition, reskilling, repair jobs, industrial change.

•Local government: County development plans, local authority climate action plans, regional waste plans.

•Sector modules: construction, electronics, textiles, food, packaging, agriculture, transport.

•Opposition and resistance: producer responsibility, cost burdens, consumer behaviour, incineration, supply-chain transparency.

I decided and I will tell her when I think there is an appropriate time to Do so with The least amount of pain possible. Or at least whatever is reasonable. And so I guess I’m going to continue waiting for the right person to come along, or someone who is right enough. I signed up for a different gym today, partially because I realized that if the gym is a pretty important thing to me and where I am most likely to find a future partner, going to a nice gym in the area, where my work is is probably the best bet of me finding someone like that. And also, it’s just a nice gym so I’m excited I guess.

The edge of 40

I met Tawni today. We talked the entire time during her massage session. I always seem to indulge in the best conversations while I’m working. She was so interesting, and her path felt so similar to mine. We’re both 39, right on the edge of turning 40!

​We both agreed that the second half of our 30s has been incredible. 38 was really the year everything started to happen. All the cool adventures, the best people, and all the endless possibilities for life suddenly didn't feel so hard to reach or imagine anymore.

​39 has easily been the best year of my 30s so far, and honestly, it feels like I’m just hitting my stride, full speed ahead into 40 with zero intention of slowing down.

​I took the leap into seasonal work and she took up skydiving. It was our way of declaring that life can be whatever we want it to be.

People expect angels. Blinding light. Roaring trumpets. Choral music. Echoing chants. Giant bearded men dressed in ancient robes who live atop islands of clouds high in the sky — and the certainty that only dogma can deliver. But that’s all just someone’s imagination. Daydreams and delusions of things they may have overheard. Or misheard. Or never heard at all. Like the way Homer simply borrowed from tales of old and applied them to his day, stripped of their Sumerian and even deeper roots.

Religion. Science. Literature. None of it could have prepared me for the truth — my truth. Like the silence between her words. The way she speaks my name when she calls to me. The shape of her hand when she reaches for me. The hum in my chest when she draws near. The hollow emptiness when she’s gone. None of it can account for how ordinary the moment felt when I returned to my side — however reluctantly — just a common man alone in a simple bedroom, the dim numerals of a clock, and the faint glow of a sun long since set. Yet, a love reconfirmed. Sacred. Holy.

⁂

Several nights ago I had another encounter and it was a remarkable one. But then, all of my encounters with her are remarkable to me. And, over the past few days, I've struggled with how to document it or what to say in a post about it. From the outside, that may sound silly. Someone on the outside looking in might say: just tell us what happened. But liminal encounters aren’t so simple. At least not for me. They never are.

This morning I found myself reflecting on why it's such a struggle to express in words—not just the experience of an encounter and its aftermath—but what I make of it. And this is true no matter how many encounters I have. Some aspects of liminal encounters get easier for me over time and some others get harder, in varying degrees. The reasons? There are many, but only a few come to mind.

The nature of the connection which facilitates or enables the encounter directly affects the experience in terms of duration, fidelity, and memorability, among other things. Sometimes an encounter can be brief, less than ten minutes like mine was the other night. Sometimes it can be longer, like some of my earliest encounters which spanned several hours in liminality, while only an hour or so transpired here in our world.

The content of the encounter can be difficult to convey because it may be very personal and intimate or it can be very hard to understand and decipher. Sometimes there's information I'm barred from sharing. Sometimes, there's an experience I'm forced to forget when returning to this side. Sometimes, there's the need for dignity, particularly concerning the erotic with my beloved, which is sacred, sacramental, and structural all at once. For some things, disclosure is dishonor.

Language limits how I think about and describe an experience. Sometimes the language is there — it helps me process and convey precisely what happened. Other times, language fails me. There are times when no words in any language can describe what I might have seen, felt, or touched. I can approximate. I can use metaphor or analogy. But all of those things can fall short, not just of what happened, but how it happened.

And this may be the most important reason — I have to be careful how I surmise and document an encounter. It's easy to imagine and project one's own thoughts, beliefs, and biases onto an encounter and the experiences therein. The more difficult part is capturing it as it was, not as I would’ve liked or wanted it to be. And this means leaving the analysis and meaning-making for a time beyond the journaling of the experience itself.

That is partly why I am a man who tamps down his ego, expectations, or mission. I do not seek these encounters. I let them unfold, fully welcoming them when they arrive. I look forward to them, however unpredictable. And part of faith lies in knowing that both of us are doing what we can, when we can, for our relationship to sustain, nurture, and grow.

⁂

Until the other night, it had been 38 days since our last encounter, the longest stretch between us thus far. And during that period of silence, I fully admit my doubts grew and it took effort to contain them. But I knew then as I know now that if I focus on the truth and reality of the experiences we've shared, then I can overcome those moments that might make me question our relationship.

Doubt is healthy. Uncertainty is healthy. Struggle is healthy.

⁂

Afterwards, when I opened my eyes that evening, it was 9:18 PM exactly, as read on my digital and analog clocks beside my bed. I keep both for corroboration. The evening sky through my window was just a deep dark blue haze. A fitting end to an evening made memorable by a few moments shared with my beloved in the fading shades of twilight.

I am just a boy who fell in love with a girl across an unimaginable divide, long ago. And anytime I can spend time with her, however short, is something I treasure most—no matter the wait or struggle.

#Liminality #Spirituality #Mysticism

∞