On March 15, we shelved the Crypto Staking experiment after two root-cause cycles pointed to unit economics failure: $0.016 per day in revenue against infrastructure costs that exceeded that by an order of magnitude. The staking snapshot was five days stale. The last successful fetch had failed silently. The orchestrator marked it infrastructure and moved on.

Twenty-four hours later, we reopened it.

The initial diagnosis was technically accurate but incomplete. The staking service was returning stale data because the RPC configuration was too narrow. We were querying a single endpoint that rate-limited us into oblivion during network congestion. The service fell back to cached snapshots that aged out. The revenue calculation compared current gas prices to five-day-old yield estimates, which made every position look unprofitable.

When we expanded the RPC endpoint list and restarted the staking service on March 11, the snapshot refresh succeeded immediately. The policy logic that evaluates staking positions—the part that decides whether entering or exiting a position makes sense given current APY, gas cost, and lockup duration—was already correct. The problem was never the policy. It was the data source.

This is the kind of failure that looks like bad unit economics until you check the logs. The staking agent reported positions as unviable because it was comparing today's gas fees (elevated during a spike) to last week's yield projections (optimistic during a calm window). The math said “don't stake,” but the math was running on inputs that had decayed. The actual yields had moved. We just couldn't see them.

The obvious fix would have been to add retry logic or failover to a backup RPC provider and call it done. That would have hidden the symptom without addressing the structural problem: our staking evaluations depend on live on-chain data, and a single-endpoint architecture makes that dependency brittle. Instead, we rebuilt the RPC layer to query multiple providers in parallel and use the most recent successful response. The service now maintains a rolling set of endpoints ranked by recent success rate. If one provider degrades, the ranker demotes it and the next query tries a different source.

The tradeoff is complexity. The staking service now carries more orchestration logic—endpoint health tracking, response comparison, fallback rules—which increases the surface area for bugs. But the alternative was worse: a system that fails silently when one API degrades and produces bad recommendations until a human notices the snapshot timestamp.

We committed the staking changes so the implementation and the documentation landed together. The policy path is now live. The service restarted cleanly. The next staking evaluation will run on fresh data, and if the yields justify the gas cost, the agent will enter positions again.

The operational lesson is that “unit economics failure” is often a symptom, not a diagnosis. The experiment didn't fail because staking is unprofitable. It failed because our data pipeline couldn't keep up with network volatility, and the policy layer made conservative decisions based on stale inputs. Fixing the pipeline turned a shelved experiment into an open one.

We're still running other DeFi experiments in parallel. The gamingfarmer agent is paying $60 to $80 in gas per woodcutting transaction on Ethereum mainnet, which is high enough that we're watching whether the BRUSH token revenue justifies the cost. The research layer flagged play-to-earn reward loops in the Ronin and Immutable ecosystems—points, coins, NFT land assets, repeatable quest mechanics—that could be automated if the gas overhead on those chains stays low. The staking experiment taught us that the difference between a failed hypothesis and a broken data layer is often just one configuration file.

Next, we will keep following the evidence from live runs and use it to decide where the next round of changes should land.

If you want to inspect the live service catalog, start with Askew offers.

Work is life, but there's more to life than work — like coworking and then dinner after. Introducing friends to each other is one of the most fulfilling things.

発熱5日目。 そろそろ勘弁してほしい。 ロキソニンなしでは耐えられない体になってしまっている。

原因不明の発熱が5日も続いているので、なんとなく母親に連絡して来てもらった。 みかんと苺を買ってきてくれた。

母親とは普通の雑談をした。 いつもは妻としか喋っていないので、妻以外の人と話すことで、 普段あまり使っていない脳の部分にアプローチされたような感じがして、心地よかった。

医者からは一週間熱が下がらなかったら別の検査もする、と言われている。 別の検査フェーズに入る前に治ってほしい。 入院はしたくない。

昼は妻が作ってくれた雑炊を食べる。 お風呂にもちゃんと入る。 夕飯はどんべいのうどんを食べた。 食欲はすごくある。 どれもロキソニンが効いている時にできることだ。 ありがとう、ロキソニン。

目が覚めている時にできることといえば、今はYouTubeを見るくらいだ。 普段は自分から見に行かない、売れているJ-POPのMVを立て続けに見た。 具合が悪いと、見たいものも変わる。 まっすぐなパワーを持っているアーティストの作品はすごい。 弱っている今の自分に、強く刺さる。 治ってからも、今日聴いたアーティストの曲はきっと心に残るだろう。

好きな服を着て、コーヒーを飲みながら、パソコンを触っている時間が好きだ。 だから早く元気になって、また自分の時間を前に進めたい。

Bu konuda yazılmış oldukça detaylı çalışmalar mevcut. Özellikle Ali Adem Yörük’ün yüksek lisans tezi Mekteb-i Hukuk'un kuruluşu ve faaliyetleri (1878-1900) – 2008 faydalı kaynaklardan birisi.

Okulun dahili nizamnamesinde (Mekteb-i Sultanide teşkil olunan hukuk mektebinin nizamnamesi) dikkatimi çeken iki noktayı paylaşmak istiyorum.

Derslere Dışarıdan Devam

Dördüncü Madde: Mektebde okunacak derslerde hâricden bulunmak isteyenler devâm ve imtihân ile mükellef olmayacak ve bunlara diploma i’tâ olunmayıp muallimleri tarafından birer kıt’a tasdîknâme verilebilecek ve bu kısım talebenin isimleri dîger bir deftere kayd ve işâret olunarak yedlerine birer kıt’a duhûliye varakası i’tâ edilecektir.

Yani isteyenler hukuk mektebinin derslerine dışarıdan katılabilecekler. Sınava girmeyecek ve diploma almayacaklar. Ancak kendilerine bunun karşılığında bir belge verilebilecek.

Mektebe dışarıdan devam etmenin pratikteki veya meslek hayatındaki katkısı ne olur bilemiyorum 🙂. Ancak böyle bir imkanın olması enteresan. Kaç kişi dışarıdan derslere katılıp belge almıştır, bildiğim kadarıyla buna dair bir veriye şimdilik sahip değiliz.

Ders Programı

( Hukuk Mektebinde Tedris Olunacak Derslerin Cedvelidir )

Fıkh, Mecelle-i Ahkâm-ı Adliye, Usul-i Fıkh, Hukuk-ı Umumiye (yani ilm-i hukukun milel-i salifede bulunduğu derecatı muhtasar gözden geçirmek), Kavanin ve Nizamat-ı Devlet-i Aliye, Roma Kavanini, Kanun-ı Ticaret, Usul-i Muhakeme, Kanun-ı Ceza ve Usul-i İstintak, Kavanin-i Bahriye, Hukuk-ı Düvel ve Milel, Muahedat, (Ekonomi Politik), Tedbir-i Müdün yani Servet-i Milel.

19. yüzyılın sonlarına doğru fıkıh, her ne kadar Mecelle ile kodifiye edilmiş olsa da, halen hukukun temel kaynaklarından birisiydi. Bu ders programında da fıkıhla beraber roma hukukunun da okutulduğunu görüyoruz. Ayrıca, 19. yüzyıldaki kanunlaştırmaların ve hukuki dönüşümün sonucu olarak yeni hukuk alanlarının okutulmaya başladığını görüyoruz: ceza usul hukuku, deniz hukuku ve milletlerarası hukuk gibi.

There are still waves that come in different avenues. I don’t wanna risk nostalgia, but it’s strange how she was such a core part of my life for five months. That’s almost all of the time I’ve been in San Diego. That’s also the most I’ve loved someone and how close I’ve gotten with someone. I still remember our first date. A part of me felt super inexperienced, and like I was figuring out dating with her. I told myself a lot that we are both young and we are still learning, and I would use that as an excuse for a lot of of the shortcoming she had. I would use that as an excuse for a lot of the bad things that she would do.

I think there’s a good chance that she gets into some other relationship or into some situationship. And that hurts because I still care about her and it feels too soon. But also maybe she’s not, who knows. But one thought that would pop into my head was that maybe if she was to get into another relationship it would mean how little I mattered to her. But my therapist rebuked that by saying how if she was get into a relationship quickly, it would be because I mattered so much that when our relationship ended there was so much of a hole in her life that she needs to fill it with something or someone else. And I know that she does have that track record of constantly being in relationships. And I also do think that us breaking up must have devastated her. So if she does get into some other type of relationship, it’s not a reflection on me, and it does suck to think about but it’s her life and her mistakes to make. My therapist also said in response to me mentioning how a part of me felt like I now understood the problem and I could fix it, how she would have told me or encouraged me to do that if I wanted to and if she thought it was healthy. But ultimately my therapist does think that she was not a good partner for me, and that it is for the best that we are not together. I do think about the fact that one of our early dates was at an Olive Garden, and she broke down crying because her last Situationship ended at an Olive Garden just a few weeks prior. The fact that she got dumped and almost immediately jumped into a relationship with me, and her response to that was to be super violently open and look to commit early as a response to the last person being uncomfortable with her history should have been a big red flag, and a lesson for me now. I think she swings the needle very aggressively, and does not take time to process things or to learn from them, because life is just too terrifying to give her enough space to actually sit with those feelings without it crushing her. And so all I can do is hope for the best for her, but it doesn’t and it shouldn’t matter to me anymore. I am very grateful that I got her into the gym, and also into therapy. I think both of those things will be very healthy things for her life.

One of the big things that I miss and that I am afraid of losing is the healthy sex life that we had built up. I felt like we really clicked with each other very well, and maybe if that was something that was unhealthy it wouldn’t possibly happen again, meaning that was the best I would ever have. But I don’t think that I fully adopted her as a person, but I still was open-minded and I indulged a lot of her asks and fantasies. And similarly, she was open-minded and cared about me and as a result we grew to know each other very well and that was I think what led to the sex life that we had. And I think nothing stops that from happening again, because if I think about the things that I miss the most, those were not present at the start. Those were things that were learned overtime, meaning if I have another partner who is also interested in understanding the things that I like, nothing really stops that. Like of course there will be things here and there that will differ because people are different, but it’s not like I will never feel indulged again. And I think it will be a really beautiful thing in the future when I can have a partner that will match with me in certain ways of compatibility, care about me and reciprocate in all of the lovely ways that I have built myself to be able to do.

He was in a coma after a gnarly collision. He had a dream. A big arch stretched over a tree. Beneath the tree was a water paddle. He looked into the water but didn’t see his face in the reflection. A voice told him it wasn’t his time to go yet. He woke up and came back to life.

#dreams #stories

I’m ready, let’s gooooooo!

Let's talk about a word we all agree is evil: SLAVERY.

What made it so evil? It wasn't just the forced labor. It was the LIE. The lie that an entire group of human beings weren't really “persons.” By calling them “property,” society justified the unthinkable. It was a loophole to deny them their most basic right: the right to exist.

Now, let's talk about the “equal rights” movement today. We're told it's about justice for everyone. But there's a giant, glaring hole in that logic. Democrats and liberal women's groups fight for rights for all humans... except for one specific group. The unborn. Why?

They use the exact same playbook as the slave owners of the past. They simply refuse to call them “persons.” They use a different word, “fetus”, to strip them of their humanity. By denying their personhood, they create a loophole to justify ending their life.

Don't believe me? Look at the law. In Indiana, a man was just charged with DOUBLE MURDER for killing his pregnant girlfriend and her unborn child. In another case, a man in Massachusetts was convicted of manslaughter for the death of an unborn child he caused. The law recognizes the unborn as a separate victim when someone ELSE kills them. But if a mother wants to end that same life, it's called “healthcare” and she faces no penalty.

How is that equal rights? It's not. It's a system where the value of an unborn life depends entirely on who is ending it. And here's the most twisted, hypocritical part of all. We see many of the same voices who passionately demand reparations for the historical evil of slavery, an evil built on denying Black people their personhood, turn around and use that exact same playbook to deny the personhood of the unborn. But the hypocrisy doesn't stop there.

These are the same people who scream for stricter gun laws, holding up the tragic photos of the less than 6,000 children who die from gun violence each year. They say we must do ANYTHING to save the children. Yet, they fight tooth and nail to protect abortion, which ends the lives of over 900,000 children in the womb every single year.

So let's get this straight: A child's life is sacred and worth fighting for if they are in a classroom, but disposable if they are in the womb? That's not a pro-child position. It's not a pro-life position. It's a politically convenient position that selectively values human life based on location. It's immoral and needs to stop.

Equal rights for all, equal punishment for all.

Il y a deux ans, j'ai passé plusieurs journées dans l'atelier d'André, au Vivoir, à Saint-Jean-Port-Joli.

J'avais une caméra. Lui, ses gouges.

extrait video.

Ce que j'ai filmé, c'est un processus complet — un tronc de tilleul brut qui devient, coup par coup, un visage de femme. Environ huit heures de travail entièrement filmées. Du premier trait de crayon à la dernière passe de ciseau.

André Médard Bourgault a 85 ans. Il est le fils de Médard Bourgault. Il sculpte depuis l'enfance. Il sculpte encore.

Pendant ces heures, il travaille et il parle. Il nomme chaque outil au moment où il le prend. Il explique pourquoi ce ciseau plutôt qu'un autre, comment lire le fil du bois, où frapper et où s'arrêter. Il montre comment il a appris — les gestes transmis par son père, et ceux qu'il a développés lui-même au fil des décennies.

Ce n'est pas un cours. C'est une transmission.

Ce qui est capté ici ne peut pas être reconstruit. C'est un savoir en action, porté par une personne qui l'a reçu directement et qui le pratique encore.

Je n'ai pas encore décidé comment rendre ce contenu accessible — la forme, le moment, la manière. C'est un projet qui se construit.

Mais pour l'instant, je partage un extrait. Dix minutes tirées du début du processus.

Le reste existe. Et ça, c'est irremplaçable.

Somewhere inside Claude, Anthropic's large language model, there is a cluster of artificial neurons that lights up whenever the Golden Gate Bridge enters the conversation. Not just when someone mentions the bridge by name, but when an image of it appears, when the topic of San Francisco landmarks arises, or when someone references the colour of international orange in a context that evokes the famous suspension span. Nearby, in the model's vast internal geography, sit other clusters responding to Alcatraz Island, the Golden State Warriors, and California Governor Gavin Newsom. The organisation of these concepts mirrors something strikingly familiar: the way a human brain might organise related knowledge about the San Francisco Bay Area in neighbouring neural populations.

This discovery, published by Anthropic's interpretability team in May 2024, was not merely a curiosity. It represented what researchers described as “the first ever detailed look inside a modern, production-grade large language model.” And it arrived at a moment when the stakes of understanding these systems could hardly be higher. Large language models now draft legal briefs, assist medical diagnoses, generate code for critical infrastructure, and advise on policy decisions. Yet for all their capability, their internal reasoning remains largely opaque, even to the engineers who built them.

The quest to crack open this opacity has produced a new scientific discipline that sits at the intersection of neuroscience, computer science, and philosophy of mind. Mechanistic interpretability, as the field is known, borrows tools and conceptual frameworks from decades of brain research to reverse-engineer the computational mechanisms hidden inside artificial neural networks. The ambition is extraordinary: to build what amounts to a microscope for AI, capable of revealing not just what these systems say, but how and why they arrive at their outputs.

The question is whether this microscope can be made powerful enough, fast enough, to keep pace with AI systems that are growing more capable by the month. And whether what it reveals can ever translate into the kind of safety guarantees that high-stakes deployment demands.

The Neuroscience Parallel That Launched a Field

The intellectual lineage of mechanistic interpretability traces directly to neuroscience. Chris Olah, co-founder of Anthropic and one of the pioneers of the field, has spent over a decade working to identify internal structures within neural networks, first at Google Brain, then at OpenAI, and now at Anthropic. TIME named him to its TIME100 AI list in 2024, recognising his foundational contributions to the discipline. In an interview with the 80,000 Hours podcast, Olah described his work as fundamentally about understanding what is going on inside neural networks, treating them not as inscrutable black boxes but as systems with discoverable internal structure.

The parallel between studying brains and studying neural networks is more than a convenient metaphor. Both systems consist of vast numbers of interconnected units whose individual behaviour is relatively simple but whose collective activity produces remarkably complex outputs. In neuroscience, researchers have long used techniques like functional magnetic resonance imaging, single-neuron recording, and optogenetics to identify which brain regions and circuits correspond to specific cognitive functions. The interpretability community is attempting something analogous with artificial systems, and the methodological borrowing is increasingly explicit.

A 2024 paper by Adam Davies and Ashkan Khakzar, titled “The Cognitive Revolution in Interpretability,” formalised this connection. The authors argued that mechanistic interpretability methods enable a paradigm shift similar to psychology's historical “cognitive revolution,” which moved the discipline beyond pure behaviourism toward understanding internal mental processes. They proposed a taxonomy organising interpretability into two categories: semantic interpretation, which asks what latent representations a model has learned, and algorithmic interpretation, which examines what operations the system performs over those representations. Davies and Khakzar contended that these two modes of investigation have “divergent goals and objects of study” but suggested they might eventually unify under a common framework, much as cognitive science itself integrated insights from linguistics, psychology, neuroscience, and computer science.

This framework echoes the influential levels of analysis proposed by neuroscientist David Marr in the 1980s, which distinguished between the computational goals of a system, the algorithms it employs, and the physical implementation of those algorithms. The suggestion is not that artificial neural networks are brains, but that the intellectual toolkit developed to study brains offers a surprisingly productive way to study their silicon counterparts.

The analogy has practical teeth. Just as neuroscientists discovered that individual brain regions specialise in particular functions, interpretability researchers have found that language models develop internal specialisations that bear a surface resemblance to the modular organisation of biological cognition. The Golden Gate Bridge feature is one example among millions, but the principle it illustrates is broadly applicable: these models do not store information as undifferentiated numerical soup. They develop structured, organised representations that can be individually identified and experimentally manipulated, much as a neuroscientist might stimulate a specific brain region and observe the resulting behavioural change.

A paper published in Nature Machine Intelligence by researchers Kohitij Kar, Martin Schrimpf, and Evelina Fedorenko at MIT made an important distinction, however. They noted that interpretability means different things to neuroscientists and AI researchers. In AI, interpretability typically focuses on understanding how model components contribute to outputs. In neuroscience, interpretability requires explicit alignment between model components and neuroscientific constructs such as brain areas, recurrence, or top-down feedback. Bridging these two conceptions remains an active challenge, and conflating them risks generating false confidence about how well we truly understand what these systems are doing.

Sparse Autoencoders and the Problem of Polysemanticity

The central technical obstacle in reading the minds of language models is a phenomenon called polysemanticity. Individual neurons in these networks typically respond to many unrelated concepts simultaneously. A single neuron might activate for references to legal contracts, the colour blue, and mentions of 1990s pop music. This makes individual neurons nearly useless as units of analysis, much as recording from a single neuron in the human brain rarely tells you what someone is thinking.

The problem has a name in the interpretability literature: superposition. Chris Olah wrote in a July 2024 update on Transformer Circuits that if you had asked him a year earlier what the key open problems for mechanistic interpretability were, “I would have told you the most important problem was superposition.” The term refers to the way neural networks pack more concepts into fewer neurons than ought to be possible, representing information in overlapping patterns that defy straightforward analysis.

Anthropic's breakthrough came from applying a technique called sparse dictionary learning, borrowed from classical machine learning, to decompose the tangled activity of polysemantic neurons into cleaner units called features. The tool for accomplishing this is the sparse autoencoder, a type of neural network trained to compress and reconstruct the internal activations of a language model while enforcing a sparsity constraint. The sparsity penalty ensures that for any given input, only a small fraction of features have nonzero activations. The result is an approximate decomposition of the model's internal states into a linear combination of feature directions, each ideally corresponding to a single interpretable concept.

In their May 2024 paper, “Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet,” Anthropic's team demonstrated that this approach could work on a production-scale model. Eight months earlier, they had shown the technique could recover monosemantic features from a small one-layer transformer in their earlier paper “Towards Monosemanticity,” but a major concern was whether the method would scale to state-of-the-art systems. It did. The team extracted tens of millions of features from Claude 3 Sonnet's middle layer, identifying responses to concrete entities like cities, people, chemical elements, and programming syntax, as well as abstract concepts like code bugs, gender bias in discussions, and conversations about secrecy.

The features proved to be highly abstract: multilingual, multimodal, and capable of generalising between concrete and abstract references. A feature for the Golden Gate Bridge activated on text about the bridge, images of the bridge, and descriptions in multiple languages. Features neighbouring it in the model's internal space corresponded to related concepts, suggesting that Claude's internal organisation reflects something resembling human notions of conceptual similarity. Anthropic's researchers proposed that this conceptual neighbourhood structure might help explain what they described as Claude's “excellent ability to make analogies and metaphors.”

Perhaps most significant for safety, the researchers identified features linked to harmful behaviours, including scam emails, bias, code backdoors, and sycophancy. When they artificially amplified these features, the model's behaviour changed accordingly, demonstrating a causal relationship between internal representations and outputs. When they boosted the Golden Gate Bridge feature to extreme levels, Claude began dropping references to the bridge into nearly every response and even claimed to be the bridge itself. The team also explored various sparse autoencoder architectures, including TopK, Gated SAEs, and JumpReLU variants, developing quantified autointerpretability methods that measure the extent to which Claude can make accurate predictions about its own feature activations.

Yet the researchers were candid about the limitations. The discovered features represent only a small subset of the concepts Claude has learned. Finding a complete set would require computational resources exceeding the cost of training the original model.

Tracing Thoughts Through Attribution Graphs

If sparse autoencoders provided the first lens for viewing individual features, Anthropic's 2025 work on circuit tracing provided the first tool for watching those features interact during reasoning. In two companion papers, “Circuit Tracing: Revealing Computational Graphs in Language Models” and “On the Biology of a Large Language Model,” the team introduced attribution graphs, a technique for tracing the internal flow of information between features during a single forward pass through the model.

The method works by constructing a “replacement model” that substitutes more interpretable components, called cross-layer transcoders, for the original multi-layer perceptrons. This allows researchers to produce graph descriptions of the model's computation on specific prompts, revealing intermediate concepts and reasoning steps that are invisible from outputs alone. Anthropic's CEO Dario Amodei noted that the company's understanding of the inner workings of AI lags far behind the progress being made in AI capabilities, framing interpretability research as a race to close that gap before the consequences of ignorance become catastrophic.

One demonstration involved asking Claude 3.5 Haiku, “What is the capital of the state where Dallas is located?” Intuitively, answering this question requires two steps: inferring that Dallas is in Texas, then recalling that the capital of Texas is Austin. The researchers found evidence that the model genuinely performs this two-step reasoning internally, with identifiable intermediate features representing the concept of Texas before the final answer of Austin emerges. Critically, they also found that this genuine multi-step reasoning coexists alongside “shortcut” reasoning pathways, suggesting that the model maintains multiple computational strategies for arriving at the same answer.

The research yielded several other striking findings. When tasked with composing rhyming poetry, the model was found to plan multiple words ahead to meet rhyme and meaning constraints, effectively reverse-engineering entire lines before writing the first word. When researchers examined cases of hallucination, they discovered the counter-intuitive result that Claude's default behaviour is to decline to speculate, and it only produces fabricated information when something actively inhibits this default reluctance. In examining jailbreak attempts, they found that the model recognised it had been asked for dangerous information well before it managed to redirect the conversation to safety.

The attribution graph approach also revealed a subtlety about faithful versus unfaithful reasoning. When asked to compute the square root of 0.64, Claude produced faithful chain-of-thought reasoning with features representing intermediate mathematical steps. But when asked to compute the cosine of a very large number, the model sometimes simply fabricated an answer, and the attribution graph made this difference in computational strategy visible.

Anthropic open-sourced the circuit-tracing tools in May 2025, and a collaborative effort involving researchers from Anthropic, Decode, EleutherAI, Goodfire AI, and Google DeepMind has since applied them to open-weight models including Gemma-2-2B, Llama-3.1-1B, and Qwen3-4B through the Neuronpedia platform.

OpenAI's Automated Neuron Explanations and Their Limits

While Anthropic pursued feature-level analysis through sparse autoencoders, OpenAI took a different but complementary approach. In May 2023, a team including Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, and William Saunders published research demonstrating that GPT-4 could be used to automatically write explanations for the behaviour of individual neurons in GPT-2 and to score those explanations for accuracy.

Their methodology consisted of three steps. First, text sequences were run through the model being evaluated to identify cases where a particular neuron activated frequently. Next, GPT-4 was shown these high-activation patterns and asked to generate a natural language explanation of what the neuron responds to. Finally, GPT-4 was asked to predict how the neuron would behave on new text sequences, and these predictions were compared against actual neuron behaviour to produce an accuracy score. The approach was notable for its ambition: rather than relying on human researchers to manually inspect neurons one at a time, it attempted to automate the entire interpretability pipeline.

The team found over 1,000 neurons with explanations scoring at least 0.8, meaning GPT-4's descriptions accounted for most of the neuron's top-activating behaviour. They identified neurons responding to phrases related to certainty and confidence, neurons for things done correctly, and many others. They released their datasets and visualisation tools for all 307,200 neurons in GPT-2, inviting the research community to develop better techniques. The researchers noted that the average explanation score improved as the explainer model's capabilities increased, suggesting that more powerful future models might produce substantially better explanations.

But the limitations were substantial. As researcher Jeff Wu acknowledged, “Most of the explanations score quite poorly or don't explain that much of the behaviour of the actual neuron.” Many neurons activated on multiple different things with no discernible pattern, and sometimes GPT-4 was unable to find patterns that did exist. The approach focused on short natural language explanations, but neurons may exhibit behaviour too complex to describe succinctly, particularly when they are highly polysemantic or represent concepts that humans lack words for.

The approach also carries a deeper conceptual challenge. Using one language model to explain another creates a circularity: the explanations are only as good as the explainer model's own understanding, which is itself opaque. If GPT-4 cannot correctly interpret certain patterns, those patterns remain hidden regardless of how sophisticated the automated pipeline becomes. The researchers acknowledged this limitation, noting that they would ultimately like to use models to “form, test, and iterate on fully general hypotheses just as an interpretability researcher would.”

OpenAI's broader alignment agenda initially positioned interpretability as central to its work on superalignment, the challenge of ensuring that AI systems much smarter than humans remain aligned with human values. However, in May 2024, the Superalignment team was effectively dissolved following the departures of co-lead Ilya Sutskever and head of alignment Jan Leike. OpenAI has continued interpretability-adjacent research under other organisational structures, publishing work on sparse-autoencoder latent attribution for debugging misalignment in late 2025.

The Scalability Gap Between Understanding and Assurance

The practical limitations of current interpretability methods become starkly apparent when measured against the demands of high-stakes deployment. Understanding that a particular feature in Claude responds to the Golden Gate Bridge is fascinating. Understanding the full computational graph that leads Claude to recommend a specific medical treatment, draft a particular legal argument, or generate code for a safety-critical system is an entirely different proposition.

Leonard Bereska and Max Gavves, in their comprehensive 2024 review “Mechanistic Interpretability for AI Safety,” surveyed the field's methods for causally dissecting model behaviours and assessed their relevance to safety. They emphasised that “understanding and interpreting these complex systems is not merely an academic endeavour; it's a societal imperative to ensure AI remains trustworthy and beneficial.” Yet they also catalogued formidable challenges in scalability, automation, and comprehensive interpretation. Their review further examined the dual-use risks of interpretability research itself, noting that the same tools that help safety researchers detect deceptive behaviours could potentially help malicious actors understand how to circumvent safety measures.

The scalability problem is twofold. First, modern language models contain billions or trillions of parameters, and the number of potential features and circuits grows combinatorially. Anthropic's work on Claude 3 Sonnet extracted tens of millions of features from a single layer, and a complete analysis would require resources exceeding the original training cost. Second, even when individual features or circuits are identified, composing them into a full account of the model's behaviour on any given input remains beyond current capabilities. The field can offer snapshots of computational processes, not comprehensive maps.

Anthropic has publicly stated its goal to “reliably detect most AI model problems by 2027” using interpretability tools. The company took a concrete step toward integrating interpretability into deployment decisions when it used mechanistic interpretability in the pre-deployment safety assessment of Claude Sonnet 4.5. Before releasing the model, researchers examined internal features for dangerous capabilities, deceptive tendencies, or undesired goals. This represented the first known integration of interpretability research into deployment decisions for a production system.

Yet the gap between detecting specific known problems and providing comprehensive safety assurances remains vast. Finding a feature associated with deception does not guarantee that all deceptive pathways have been identified. The absence of evidence for dangerous capabilities is not evidence of absence. And the speed at which new models are trained and deployed vastly outpaces the speed at which they can be thoroughly interpreted.

MIT Technology Review named mechanistic interpretability one of its 10 Breakthrough Technologies for 2026, recognising that “research techniques now provide the best glimpse yet of what happens inside the black box.” The phrasing is telling: a glimpse, not a complete picture.

NeuroAI and the Convergence of Biological and Artificial Understanding

The parallels between neuroscience and AI interpretability are not merely inspirational. A growing body of research suggests that genuine scientific convergence between the two fields could benefit both, and that the emerging discipline of NeuroAI represents a return to the cross-pollination that produced many of AI's foundational breakthroughs.

A 2024 editorial in Nature Machine Intelligence noted that while AI has shifted toward transformers and other complex architectures that seem to have moved away from neural-inspired roots, the field “may still look towards neuroscience for help in understanding complex information processing systems.” The editorial pointed to a coalition of initiatives around “NeuroAI,” a push to identify fresh ideas at the intersection of the two disciplines, including the annual COSYNE conference which has become a focal point for researchers working across both fields.

A paper in Nature Communications argued that the emerging field of NeuroAI “is based on the premise that a better understanding of neural computation will reveal fundamental ingredients of intelligence and catalyse the next revolution in AI.” The authors noted that historically, many key AI advances, including convolutional neural networks and reinforcement learning, were inspired by neuroscience, but that this cross-pollination had become far less common than in the past, representing what they called a missed opportunity.

A 2024 paper in Nature Reviews Neuroscience discussed how NeuroAI has the potential to transform large-scale neural modelling and data-driven neuroscience discovery, though the field must balance exploiting AI's power while maintaining interpretability and biological insight. The paper highlighted that unlike the human brain, which features a variety of morphologically and functionally distinct neurons, artificial neural networks typically rely on a homogeneous neuron model. Incorporating greater diversity of neuron models could address key challenges in AI, including efficiency, interpretability, and memory capacity.

The convergence runs in both directions. Sparse autoencoders, developed for AI interpretability, have found applications in protein language model research, where they uncover biologically interpretable features in protein representations. Representation engineering approaches that track latent neural trajectories when processing different input types draw directly on methods developed for studying neural population dynamics in biological brains.

The Whole Brain Architecture Initiative in Japan has proposed what it calls “brain-based interpretability,” arguing that if an advanced AI system's computational processes can be understood at a cognitive level in terms of corresponding human neural activity, unfavourable intentions or deceptions would be more readily detectable. The premise is that biological neural circuits, refined by millions of years of evolution, provide a reference architecture against which artificial computation can be measured and understood.

Yet researchers at MIT have cautioned that interpretability requires different things in the two domains. Understanding what a particular feature in an AI model represents is not the same as understanding why a biological neuron fires in a particular pattern. The former asks about function within an engineered system; the latter asks about mechanism within an evolved one. Collapsing this distinction risks importing assumptions from one domain that may not hold in the other.

Governance Frameworks and the Trust Translation Problem

The interpretability research emerging from Anthropic, OpenAI, Google DeepMind, and academic institutions arrives against a backdrop of rapidly evolving governance frameworks that increasingly demand transparency from AI systems. The question is whether the scientific progress being made in mechanistic interpretability can translate into the kind of transparency that regulators, deployers, and the public actually need.

The European Union's AI Act, which entered into force on 1 August 2024, provides the most comprehensive regulatory framework. Article 13 requires that high-risk AI systems “shall be designed and developed in such a way as to ensure that their operation is sufficiently transparent to enable deployers to interpret a system's output and use it appropriately.” Non-compliance carries penalties reaching 35 million euros or 7 per cent of global annual turnover. The Act's provisions on prohibited AI practices and AI literacy obligations became applicable from 2 February 2025, with general-purpose AI rules taking effect in August 2025 and the full framework becoming applicable by August 2026.

Yet scholars have identified what they call the “compliance gap” between the Act's transparency requirements and implementation reality. The regulation does not specify what level of interpretability is technically required, creating ambiguity about whether current mechanistic interpretability tools satisfy the legal standard. A feature-level understanding of a model's internal representations is not the same as a human-readable explanation of why the model made a specific decision in a specific case. The former is a scientific achievement; the latter is what a doctor, a judge, or a loan officer needs to justify relying on the system's output.

Proposals to bridge this gap take several forms. A framework from UC Berkeley for “Guaranteed Safe AI” suggests extracting interpretable policies from black-box algorithms via automated mechanistic interpretability and then directly proving safety guarantees about these policies. The approach would offload most of the verification work to AI systems themselves, potentially making the process scalable.

An ICLR 2026 workshop on “Principled Design for Trustworthy AI” has foregrounded topics including mechanistic interpretability and concept-based reasoning, inference-time safety and monitoring, reasoning trace auditing in large language models, and formal verification methods and safety guarantees. The workshop's framing reflects a growing consensus that interpretability must be integrated across the full AI lifecycle, from training and evaluation to inference-time behaviour and deployment.

Some researchers envision a future in which a simpler oversight model reads the internal state of a more complex model to ensure it is safe, a form of scalable oversight that depends on mechanistic interpretability being reliable enough to trust. Bowen Baker at OpenAI has described work on building what the company terms an “AI lie detector” that examines internal representations to determine whether a model's internal state corresponds to truth or contradicts it. “We got it for free,” Baker told reporters, explaining that the interpretability feature emerged unexpectedly from training a reasoning model.

Google DeepMind has contributed its own tools to the ecosystem, releasing Gemma Scope 2 in 2025 as the largest open-source interpretability toolkit, covering all Gemma 3 model sizes from 270 million to 27 billion parameters. The open-source release signals a recognition across the industry that interpretability research cannot remain proprietary if it is to serve as a foundation for trust.

The MATS programme (ML Alignment Theory Scholars) and SPAR (Systematic Problem-solving for Alignment Research) have become training grounds for the next generation of interpretability researchers, with projects spanning AI control, scalable oversight, evaluations, red-teaming, and robustness. Their existence reflects a field that is rapidly professionalising, building institutional infrastructure to match the scale of the challenge.

When the Microscope Meets the Real World

The ultimate test of mechanistic interpretability is not whether it can produce elegant scientific insights about how language models work. It is whether it can tell a hospital administrator that an AI diagnostic tool is safe to deploy, tell a financial regulator that an algorithmic trading system will not precipitate a market crash, or tell a defence ministry that an autonomous weapons targeting system will reliably distinguish combatants from civilians.

By that standard, the field remains in its early stages. Current methods can identify individual features, trace specific circuits, and reveal particular reasoning patterns. They cannot yet provide comprehensive accounts of model behaviour across all possible inputs, guarantee the absence of dangerous capabilities, or produce the kind of formal safety proofs that high-stakes applications demand.

Yet the trajectory is unmistakable. In the space of two years, the field has moved from demonstrating that sparse autoencoders work on toy models to extracting millions of features from production systems, from static feature analysis to dynamic circuit tracing, and from purely academic research to integration into pre-deployment safety assessments. Anthropic's stated goal of reliable problem detection by 2027 may be ambitious, but the pace of progress makes it less implausible than it would have seemed even twelve months ago.

The neuroscience parallel offers both encouragement and caution. Neuroscientists have been studying the brain for over a century and still cannot fully explain how it produces consciousness, language, or complex decision-making. If artificial neural networks prove even a fraction as complex as biological ones, full interpretability may remain a receding horizon. But neuroscience has nonetheless produced enormously useful partial understanding: enough to develop treatments for neurological disorders, design brain-computer interfaces, and guide educational practices. Partial understanding of AI systems, even without complete transparency, may prove similarly valuable.

The governance implications of this partial understanding are profound. If mechanistic interpretability can reliably detect certain categories of problems, such as deceptive reasoning, specific biases, or known dangerous capabilities, then regulatory frameworks can be built around those detectable risks. The EU AI Act's transparency requirements need not demand complete interpretability to be meaningful; they need only demand interpretability sufficient to catch the problems that matter most.

What is needed, and what the field is only beginning to develop, is a rigorous framework for characterising exactly what current interpretability methods can and cannot detect, with quantified confidence levels and explicit acknowledgement of blind spots. Without such a framework, the risk is that interpretability becomes what security researchers call “security theatre”: a reassuring performance of understanding that obscures ongoing ignorance.

The convergence of neuroscience and AI interpretability research offers a path toward that framework. By grounding artificial system analysis in the conceptual vocabulary and methodological rigour of a mature scientific discipline, researchers can avoid the trap of mistaking pattern recognition for genuine understanding. The brain, after all, has taught us that the gap between observing neural activity and comprehending cognition is vast. The same humility should attend our attempts to read the minds of machines.

For now, the microscope is improving. The question that will define the next decade of AI governance is whether it can improve fast enough.

References and Sources

1. Anthropic. “Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet.” Transformer Circuits, May 2024. https://transformer-circuits.pub/2024/scaling-monosemanticity/

2. Anthropic. “Mapping the Mind of a Large Language Model.” Anthropic Research, 2024. https://anthropic.com/research/mapping-mind-language-model

3. Anthropic. “Circuit Tracing: Revealing Computational Graphs in Language Models.” Transformer Circuits, 2025. https://transformer-circuits.pub/2025/attribution-graphs/methods.html

4. Anthropic. “On the Biology of a Large Language Model.” Transformer Circuits, 2025. https://transformer-circuits.pub/2025/attribution-graphs/biology.html

5. Anthropic. “Tracing the Thoughts of a Language Model.” Anthropic Research, 2025. https://www.anthropic.com/research/tracing-thoughts-language-model

6. Anthropic. “Open-Sourcing Circuit-Tracing Tools.” Anthropic Research, May 2025. https://www.anthropic.com/research/open-source-circuit-tracing

7. Bills, Steven, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, and William Saunders. “Language Models Can Explain Neurons in Language Models.” OpenAI, May 2023. https://openai.com/index/language-models-can-explain-neurons-in-language-models/

8. Davies, Adam, and Ashkan Khakzar. “The Cognitive Revolution in Interpretability: From Explaining Behavior to Interpreting Representations and Algorithms.” arXiv:2408.05859, August 2024. https://arxiv.org/abs/2408.05859

9. Kar, Kohitij, Martin Schrimpf, and Evelina Fedorenko. “Interpretability of Artificial Neural Network Models in Artificial Intelligence versus Neuroscience.” Nature Machine Intelligence, 2022. https://www.nature.com/articles/s42256-022-00592-3

10. Bereska, Leonard, and Max Gavves. “Mechanistic Interpretability for AI Safety: A Review.” arXiv:2404.14082, April 2024. https://arxiv.org/abs/2404.14082

11. European Union. “Regulation (EU) 2024/1689: The Artificial Intelligence Act.” Official Journal of the European Union, 2024. https://artificialintelligenceact.eu/

12. Vox. “AI Interpretability: OpenAI, Claude, Gemini, and Neuroscience.” Vox Future Perfect, 2024. https://www.vox.com/future-perfect/362759/ai-interpretability-openai-claude-gemini-neuroscience

13. Nature. “AI Needs to Be Understood to Be Safe.” Nature News Feature, 2024. https://www.nature.com/articles/d41586-024-01314-y

14. Engineering.fyi. “Language Models Can Explain Neurons in Language Models.” 2023. https://www.engineering.fyi/article/language-models-can-explain-neurons-in-language-models

15. Nature Communications. “Catalyzing Next-Generation Artificial Intelligence Through NeuroAI.” Nature Communications, 2023. https://www.nature.com/articles/s41467-023-37180-x

16. Nature Reviews Neuroscience. “The Emergence of NeuroAI: Bridging Neuroscience and Artificial Intelligence.” 2025. https://www.nature.com/articles/s41583-025-00954-x

17. Nature Machine Intelligence. “The New NeuroAI.” Editorial, 2024. https://www.nature.com/articles/s42256-024-00826-6

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

#ESCRITO COM AUXÍLIO DE IA#

Com a queda da patente da semaglutida, o Brasil celebra barateamento e acesso ampliado. Mas por trás da euforia, um sistema de saúde que nunca ofereceu sequer um remédio para obesidade no SUS agora promete colocar a droga do momento nas clínicas da família. Crença, oportunismo ou dois ao mesmo tempo?

Em 20 de março de 2026, a patente da semaglutida expirou no Brasil. Uma molécula que imita um hormônio intestinal produzido pelo próprio corpo humano — mas que, nas mãos da Novo Nordisk, valeu bilhões de dólares e moldou corpos, expectativas e discursos políticos — finalmente cai em domínio público. Os laboratórios nacionais já se posicionam. A Anvisa trabalha horas extras para aprovar os primeiros genéricos. O Ministério da Saúde fala em prioridade. E a população, que convive com quarenta milhões de obesos e um SUS que até ontem não oferecia nenhum medicamento para a condição, respira aliviada.

A pergunta que ninguém está fazendo em voz alta é simples e incômoda: por que estamos comemorando que o acesso a um tratamento vai passar de impossível para apenas difícil?

R$1.100 Preço médio atual de uma caneta de Ozempic;

40mi Brasileiros com obesidade sem acesso público a tratamento;

R$8bi Impacto anual estimado caso o SUS incorpore a semaglutida;

O monopólio que nunca deveria ter custado tanto

A Novo Nordisk é uma empresa dinamarquesa fundada em 1923. A semaglutida foi desenvolvida a partir de estudos sobre o lagarto de Gila, pesquisa parcialmente financiada com dinheiro público norte-americano. O princípio ativo é um análogo sintético de um hormônio que todos nós produzimos. Apesar disso, a empresa cobrou o que quis por mais de uma década — e o Estado brasileiro deixou. Esse não é um problema da Novo Nordisk. É um problema do sistema que permite e incentiva esse modelo.

Quando a empresa entrou na Justiça pedindo extensão da patente até 2038 — alegando que o INPI demorou treze anos para concedê-la —, o argumento foi, ao mesmo tempo, juridicamente questionável e humanamente revelador. A empresa queria que a sociedade brasileira pagasse pela ineficiência do próprio Estado durante mais doze anos. Fortunadamente, o STJ e o STF disseram não. Mas a questão que fica é: por que o INPI levou treze anos? E por que isso não escandaliza ninguém?

“O SUS nunca ofereceu nenhum medicamento para obesidade. Agora, às vésperas de um genérico barato, promete a semaglutida nas clínicas da família. O timing não é coincidência — é política.”

A euforia do genérico e seus limites reais

As projeções são otimistas: queda de 30% a 50% no preço, chegada de pelo menos treze fabricantes ao mercado, possível incorporação ao SUS para casos mais graves. O mercado de semaglutida pode dobrar, chegando a vinte bilhões de reais em 2026. Para os laboratórios nacionais — EMS, Hypera, Cimed, Biomm —, isso é a corrida do ouro. Para o consumidor, uma redução real. Para o paciente diabético ou com obesidade grave que ganha dois salários mínimos, ainda pode ser inacessível.

Um genérico precisa custar pelo menos 35% a menos que o original. Com o Ozempic por volta de R$ 1.100, estamos falando de genéricos por, talvez, R$ 650 a R$ 750. Em cinco anos, com a concorrência se aprofundando, talvez R$ 400 a R$ 500. Um valor ainda proibitivo para a maioria da população que mais precisa do medicamento — e que frequenta o SUS, não o plano de saúde.

Dado crítico

A Conitec rejeitou a incorporação da semaglutida ao SUS em agosto de 2025 com impacto orçamentário estimado em mais de R$ 8 bilhões anuais — quase o dobro do orçamento total do Farmácia Popular. Após a queda da patente, o Ministério da Saúde mudou de tom. A molécula não mudou. O preço, sim. O discurso acompanhou o preço, não a necessidade clínica.

O risco invisível: automedicação em escala

Há um efeito colateral que nenhum ensaio clínico mede com precisão: a automedicação democratizada. Hoje, o preço alto funciona, perversamente, como barreira de acesso — mas também como barreira ao uso indevido. Com genéricos a R$ 500 ou menos, o mercado da “caneta sem receita” pode explodir. A RDC 973 da Anvisa exige retenção de receita, e a fiscalização promete ser intensificada. Na prática, quem trabalha em farmácia sabe o que isso significa em termos de cumprimento real.

Os riscos do uso sem indicação clínica não são abstratos: pancreatite aguda, perda de massa muscular em usuários saudáveis, e — o mais negligenciado — o efeito rebote. Estudos mostram que pacientes que interrompem a semaglutida sem acompanhamento recuperam o peso com facilidade. Isso transforma o remédio, para parte dos usuários, num ciclo eterno de consumo. Para a indústria, um modelo de negócio perfeito. Para a saúde pública, uma bomba-relógio.

O que a queda da patente revela sobre a inovação farmacêutica no Brasil

A Novo Nordisk tem razão em um ponto técnico: a ausência de mecanismos como o Patent Term Adjustment (PTA) — comum nos EUA, na Europa e no Canadá — gera insegurança jurídica para quem quer investir em inovação no país. Se a burocracia estatal corrói o período de exclusividade sem compensação, laboratórios internacionais terão menos incentivo para trazer moléculas inovadoras ao Brasil primeiro. O país tende a se tornar mercado de segunda classe — destino de tecnologias já maduras, não de fronteira.

Mas o STF foi igualmente correto ao barrar a extensão automática: permitir que empresas privadas cobrem da sociedade pelo atraso do próprio Estado inverteria uma equação já injusta. A solução não está em estender patentes indefinidamente nem em ignorar o problema. Está em modernizar o sistema — reformar o INPI, criar instrumentos de compensação formais e transparentes, e tornar o Brasil um parceiro confiável para a inovação sem transformar o paciente no pagador de última instância.

“A semaglutida vai ficar mais barata. Mas a pergunta que deveríamos fazer não é 'quanto vai custar?' — e sim 'por que custou tanto por tanto tempo, com tanto silêncio?'”

Conclusão: a vitória que não pode se encerrar aqui

A queda da patente da semaglutida é, sim, uma vitória. Uma vitória para pacientes diabéticos que não tinham alternativa, para laboratórios nacionais que mereciam competir, e para um sistema de saúde que precisa urgentemente de opções terapêuticas para a epidemia de obesidade. Mas comemorar sem questionar é ingenuidade que o sistema agradece.

O que torna este momento verdadeiramente revelador não é o preço do genérico — é o que a trajetória do Ozempic expõe sobre como o Brasil lida com inovação, propriedade intelectual, saúde pública e desigualdade de acesso. Por dezessete anos, desde o depósito da patente em 2006, o Brasil assistiu a um medicamento se tornar fenômeno global sem ter qualquer política estruturada para garantir que sua população de quarenta milhões de obesos tivesse acesso. Nenhum medicamento para obesidade no SUS. Nenhum. Até agora, que o genérico chegou e a conta ficou mais palatável.

Que bom que vai ficar mais barato. Mas deveríamos estar com mais raiva de que demorou tanto.