In Summary: * My night's basketball game has been an early NCAA men's non-conference match-up between the Gonzaga Bulldogs and the Kentucky Wildcats playing at the Bridgestone Arena in Nashville TN as part of the Music City Madness Tournament. With Gonzaga leading from the opening tip and winning by a score of 94 to 59, there was no tension or excitement to interrupt the relaxing monotonous call of the game. Nice. It leaves me with an easy peaceful state of mind before bedtime.

Prayers, etc.: * My daily prayers

Health Metrics: * bw= 220.57 lbs. * bp= 133/79 (63)

Exercise: * kegel pelvic floor exercise, half squats, calf raises, wall push-ups

Diet: * 07:00 – 1 peanut butter sandwich * 08:00 – 1 fresh orange * 12:00 – baked fish and vegetables * 16:30 – 1 bean & cheese, breakfast taco

Activities, Chores, etc.: * 04:30 – listen to local news talk radio * 06:10 – bank accounts activity monitored * 06:55 – read, pray, follow news reports from various sources * 12:00 t0 14:00 – watch old game shows and eat lunch at home with Sylvia * 14:10 – follow news reports from various sources * 17:00 – listening to The Joe Pags Show * 18:00 – listening to NCAA men's basketball, Gonzaga Bulldogs at Kentucky Wildcats. * 20:10 – Gonzaga wins, 94 to 59. Time now to put on some relaxing music, finish my night prayers, and quietly read my way into an early bedtime.

Chess: * 14:30 – moved in all pending CC games

The pharmaceutical industry has always been a high-stakes gamble. For every drug that reaches pharmacy shelves, thousands of molecular candidates fall by the wayside, casualties of a discovery process that devours billions of pounds and stretches across decades. The traditional odds are brutally unfavourable: roughly one in 5,000 compounds that enter preclinical testing eventually wins regulatory approval, and the journey typically consumes 10 to 15 years and costs upwards of £2 billion. Now, artificial intelligence promises to rewrite these economics entirely, and the early evidence suggests it might actually deliver.

In laboratories from Boston to Shanghai, scientists are watching algorithms design antibodies from scratch, predict protein structures with atomic precision, and compress drug discovery timelines from years into months. These aren't incremental improvements but fundamental shifts in how pharmaceutical science operates, driven by machine learning systems that can process biological data at scales and speeds no human team could match. The question is no longer whether AI can accelerate drug discovery, but rather how reliably it can do so across diverse therapeutic areas, and what safeguards the industry needs to translate computational leads into medicines that are both safe and effective.

The Computational Revolution in Molecular Design

Consider David Baker's laboratory at the University of Washington's Institute for Protein Design. In work published during 2024, Baker's team used a generative AI model called RFdiffusion to design antibodies entirely from scratch, achieving what the field had long considered a moonshot goal. These weren't antibodies optimised from existing templates but wholly novel molecules, computationally conceived and validated through rigorous experimental testing including cryo-electron microscopy. The structural agreement between predicted and actual configurations was remarkable, with root-mean-square deviation values as low as 0.3 angstroms for individual complementarity-determining regions.

Previously, no AI systems had demonstrated they could produce high-quality lead antibodies from scratch in a way that generalises across protein targets and antibody formats. Baker's team reported AI-aided discovery of antibodies that bind to an influenza protein common to all viral strains, plus antibodies that block a potent toxin produced by Clostridium difficile. By shifting antibody design from trial-and-error wet laboratory processes to rational computational workflows, the laboratory compressed discovery timelines from years to weeks.

The implications ripple across the pharmaceutical landscape. Nabla Bio created JAM, an AI system designed to generate de novo antibodies with favourable affinities across soluble and difficult-to-drug membrane proteins, including CXCR7, one member of the family of approximately 800 GPCR membrane proteins that have historically resisted traditional antibody development.

Absci announced the ability to create and validate de novo antibodies in silico using zero-shot generative AI. The company reported designing the first antibody capable of binding to a protein target on HIV known as the caldera region, a previously difficult-to-drug epitope. In February 2024, Absci initiated IND-enabling studies for ABS-101, a potential best-in-class anti-TL1A antibody, expecting to submit an investigational new drug application in the first quarter of 2025. The company claims its Integrated Drug Creation platform can advance AI-designed development candidates in as few as 14 months, potentially reducing the journey from concept to clinic from six years down to 18-24 months.

Where AI Delivers Maximum Impact

The drug discovery pipeline comprises distinct phases, each with characteristic challenges and failure modes. AI's impact varies dramatically depending on which stage you examine. The technology delivers its most profound advantages in early discovery: target identification, hit discovery, and lead optimisation, where computational horsepower can evaluate millions of molecular candidates simultaneously.

Target identification involves finding the biological molecules, typically proteins, that play causal roles in disease. Recursion Pharmaceuticals built the Recursion Operating System, a platform that has generated one of the largest fit-for-purpose proprietary biological and chemical datasets globally, spanning 65 petabytes across phenomics, transcriptomics, in vivo data, proteomics, and ADME characteristics. Their automated wet laboratory utilises robotics and computer vision to capture millions of cell experiments weekly, feeding data into machine learning models that identify novel therapeutic targets with unprecedented systematic rigour.

Once targets are identified, hit discovery begins. This is where AI's pattern recognition capabilities shine brightest. Insilico Medicine used AI to identify a novel drug target and design a lead molecule for idiopathic pulmonary fibrosis, advancing it through preclinical testing to Phase I readiness in under 18 months, a timeline that would have been impossible using traditional methods. The company's platform nominated ISM5411 as a preclinical candidate for inflammatory bowel disease in January 2022 after only 12 months to synthesise and screen approximately 115 molecules. Their fastest preclinical candidate nomination was nine months for the QPCTL programme.

Lead optimisation also benefits substantially from AI. Exscientia reports a 70 percent faster lead-design cycle coupled with an 80 percent reduction in upfront capital. The molecule DSP-1181, developed with Sumitomo Dainippon Pharma, moved from project start to clinical trial in 12 months, compared to approximately five years normally. Exscientia was the first company to advance an AI-designed drug candidate into clinical trials.

However, AI's advantages diminish in later pipeline stages. Clinical trial design, patient recruitment, and safety monitoring still require substantial human expertise and regulatory oversight. As compounds progress from Phase I through Phase III studies, rate-limiting factors shift from molecular design to clinical execution and regulatory review.

The Reliability Question

The pharmaceutical industry has grown justifiably cautious about overhyped technologies. What does the empirical evidence reveal about AI's actual success rates?

The early data looks genuinely promising. As of December 2023, AI-discovered drugs that completed Phase I trials showed success rates of 80 to 90 percent, substantially higher than the roughly 40 percent success rate for traditionally discovered molecules. Out of 24 AI-designed molecules that entered Phase I testing, 21 successfully passed, yielding an 85 to 88 percent success rate nearly double the historical benchmark.

For Phase II trials, success rates for AI-discovered molecules sit around 40 percent, comparable to historical averages. This reflects the reality that Phase II trials test proof-of-concept in patient populations, where biological complexity creates challenges that even sophisticated AI cannot fully predict from preclinical data. If current trends continue, analysts project the probability of a molecule successfully navigating all clinical phases could increase from 5 to 10 percent historically to 9 to 18 percent for AI-discovered candidates.

The number of AI-discovered drug candidates entering clinical stages is growing exponentially. From three candidates in 2016, the count reached 17 in 2020 and 67 in 2023. AI-native biotechnology companies and their pharmaceutical partners have entered 75 AI-discovered molecules into clinical trials since 2015, demonstrating a compound annual growth rate exceeding 60 percent.

Insilico Medicine provides a useful case study. By December 31, 2024, the company had nominated 22 developmental candidates from its own chemistry and biology platform, with 10 programmes progressing to human clinical stage, four completed Phase I studies, and one completed Phase IIa. In January 2025, Insilico announced positive results from two Phase I studies in Australia and China of ISM5411, a novel gut-restricted PHD inhibitor that proved generally safe and well tolerated.

The company's lead drug INS018_055 (rentosertib) reached Phase IIa trials for idiopathic pulmonary fibrosis, a devastating disease with limited treatment options. Following publication of a Nature Biotechnology paper in early 2024 presenting the entire journey from AI algorithms to Phase II clinical trials, Insilico announced positive results showing favourable safety and dose-dependent response in forced vital capacity after only 12 weeks. The company is preparing a Phase IIb proof-of-concept study to be initiated in 2025, representing a critical test of whether AI-discovered drugs can demonstrate the robust efficacy needed for regulatory approval.

Yet not everything proceeds smoothly. Recursion Pharmaceuticals, despite securing partnerships with Roche, Sanofi, and Bayer, recently announced it was shelving three advanced drug prospects following its 2024 merger with Exscientia. The company halted development of drugs for cerebral cavernous malformation and neurofibromatosis type II in mid-stage testing, choosing to focus resources on programmes with larger commercial potential. Exscientia itself had to deprioritise its cancer drug EXS-21546 after early-stage trials and pare back its pipeline to focus on CDK7 and LSD1 oncology programmes. These strategic retreats illustrate that AI-discovered drugs face the same clinical and commercial risks as traditionally discovered molecules.

The Validation Imperative

The gap between computational prediction and experimental reality represents one of the most critical challenges. Machine learning models train on available data, but biological systems exhibit complexity that even sophisticated algorithms struggle to capture fully, creating an imperative for rigorous experimental validation.

Traditional QSAR-based models faced problems including small training sets, experimental data errors, and lack of thorough validation. Modern AI approaches address these limitations through iterative cycles integrating computational prediction with wet laboratory testing. Robust iteration between teams proves critical because data underlying any model remains limited and biased by the experiments that generated it.

Companies like Absci report that initially, their computational designs exhibited modest affinity, but subsequent affinity maturation techniques such as OrthoRep improved binding strength to single-digit nanomolar levels whilst preserving epitope selectivity. This demonstrates that AI provides excellent starting points, but optimisation through experimental iteration often proves necessary.

The validation paradigm is shifting. In traditional drug discovery, wet laboratory experiments dominated from start to finish. In the emerging paradigm, in silico experiments could take projects almost to the endpoint, with wet laboratory validation serving as final confirmation that ensures only the best candidates proceed to clinical trials.

Generate Biomedicines exemplifies this integrated approach. The company's Generative Biology platform trains on the entire compendium of protein structures and sequences found in nature, supplemented with proprietary experimental data, to learn generalisable rules by which amino acid sequences encode protein structure and function. Their generative model Chroma can produce designs for proteins with specific properties. To validate predictions, Generate opened a cryo-electron microscopy laboratory in Andover, Massachusetts, that provides high-resolution structural data feeding back into the AI models.

However, challenges persist. Generative AI often suggests compounds that prove challenging or impossible to synthesise, or that lack drug-like properties such as appropriate solubility, stability, or bioavailability. Up to 40 percent of antibody candidates fail in clinical trials due to unanticipated developability issues, costing billions of pounds annually.

Intellectual Property in the Age of Algorithmic Invention

Who owns a drug that an algorithm designed? This question opens a labyrinth of legal complexity that the pharmaceutical and biotechnology industries are only beginning to navigate.

Under United States patent law, inventorship is strictly reserved for natural persons. The 2022 Thaler v. Vidal decision rejected patent applications listing DABUS, an AI system, as the sole inventor. However, the United States Patent and Trademark Office's 2024 guidance clarified that AI-assisted inventions remain patentable if a human provides a significant contribution to either conception or reduction to practice.

The critical phrase is “significant contribution.” In most cases, a human merely reducing an AI invention to practice does not constitute sufficient contribution. However, iterating on and improving an AI output can clear that bar. Companies that develop AI systems focused on specific issues have indicia of human contribution from the outset, for example by identifying binding affinity requirements and in vivo performance specifications, then developing AI platforms to generate drug candidates with those properties.

This creates strategic imperatives for documentation. It's critical to thoroughly document the inventive process including both AI and human contributions, detailing specific acts humans undertook beyond mere verification of AI outputs. Without such documentation, companies risk having patent applications rejected or granted patents later invalidated.

International jurisdictions add complexity. The European Patent Office requires “technical contribution” beyond mere data analysis. AI drug discovery tools need to improve experimental methods or manufacturing processes to qualify under EPO standards. China's revised 2024 guidelines allow AI systems to be named as co-inventors if humans oversee their output, though enforcement remains inconsistent.

Pharmaceutical companies increasingly turn to hybrid approaches. Relay Therapeutics combines strategies by patenting drug candidates whilst keeping molecular dynamics simulations confidential. Yet complications arise: whilst Recursion Pharmaceuticals has multiple AI-optimised small molecule compounds in clinical development, several (REC-2282 and REC-4881) were known and patented by other parties, requiring Recursion to obtain licences. Even sophisticated AI systems may rediscover molecules that already exist in the intellectual property landscape.

Regulatory Pathways

Regulatory agencies face an unprecedented challenge: how do you evaluate drugs designed by systems you cannot fully interrogate? The United States Food and Drug Administration issued its first guidance on the use of AI for drug and biological product development in January 2025, providing a risk-based framework for sponsors to assess and establish the credibility of an AI model for particular contexts of use.

This represents a critical milestone. Since 2016, the use of AI in drug development and regulatory submissions has exponentially increased. CDER's experience includes over 500 submissions with AI components from 2016 to 2023, yet formal guidance remained absent until now. The framework addresses how sponsors should validate AI models, document training data provenance and quality, and demonstrate that model outputs are reliable for their intended regulatory purpose.

The fundamental principle remains unchanged: new drugs must undergo rigorous testing and evaluation to gain FDA approval regardless of how they were designed. However, this can prove more challenging for generative AI because underlying biology and mechanisms of action may not be sufficiently understood. When an AI system identifies a novel target through pattern recognition across vast datasets, human researchers may struggle to articulate the mechanistic rationale that regulators typically expect.

Regulatory submissions for AI-designed drugs need to include not only traditional preclinical and clinical data, but also detailed information about the AI system itself: training data sources and quality, model architecture and validation, limitations and potential biases, and the rationale for trusting model predictions.

As of 2024, there are no on-market medications developed using an AI-first pipeline, though many are progressing through clinical trials. The race to become first carries both prestige and risk: the inaugural approval will establish precedents that shape regulatory expectations for years to come.

The medical device sector provides instructive precedents. Through 2025, the FDA has authorised over 1,000 AI-enabled medical devices, developing institutional experience with evaluating AI systems. Drug regulation, however, presents distinct challenges: whilst medical device AI often assists human decision-making, drug discovery AI makes autonomous design decisions that directly determine molecular structures.

Business Models and Partnership Structures

The business models emerging at the intersection of AI and drug discovery exhibit remarkable diversity. Some companies pursue proprietary pipelines, others position themselves as platform providers, and many adopt hybrid approaches balancing proprietary programmes with strategic partnerships.

Recent deals demonstrate substantial valuations attached to proven AI capabilities. AstraZeneca agreed to pay more than £4 billion to CSPC Pharmaceutical Group for access to its AI platform and a portfolio of preclinical cancer drugs, one of the largest AI biotech deals to date. Sanofi unveiled a £1.3 billion agreement with Earendil Labs in April 2024. Pfizer invested £15 million in equity with CytoReason, with the option to licence the platform in a deal that could reach £85 million over five years.

Generate Biomedicines secured a collaboration with Amgen worth up to £1.5 billion across five co-development programmes in oncology, immunology, and infectious diseases. These deals reflect pharmaceutical companies' recognition that internal AI capabilities may lag behind specialised AI biotechs, making strategic partnerships the fastest route to accessing cutting-edge technology.

Morgan Stanley Research believes that modest improvements in early-stage drug development success rates enabled by AI could lead to an additional 50 novel therapies over a 10-year period, translating to more than £40 billion in opportunity. The McKinsey Global Institute projects generative AI will deliver £48 to £88 billion annually in pharmaceutical value, largely by accelerating early discovery and optimising resource allocation.

Partnership structures must address complex questions around intellectual property allocation, development responsibilities, financial terms, and commercialisation rights. Effective governance structures, both formal contractual mechanisms and informal collaborative norms, prove essential for partnership success.

The high-profile merger between Recursion Pharmaceuticals and Exscientia, announced in August 2024 with a combined valuation of approximately £430 million, represents consolidation amongst AI biotechs to achieve scale advantages and diversified pipelines. The merged entity subsequently announced pipeline cuts to extend its financial runway into mid-2027, illustrating ongoing capital efficiency pressures facing the sector.

The AlphaFold Revolution

No discussion of AI in drug discovery can ignore AlphaFold, DeepMind's protein structure prediction system that won the 14th Critical Assessment of Structure Prediction competition in December 2020. Considered by many as AI's greatest contribution to scientific fields and one of the most important scientific breakthroughs of the 21st century, AlphaFold2 reshaped structural biology and created unprecedented opportunities for research.

The system's achievement was predicting protein structures with experimental-grade accuracy from amino acid sequences alone. For decades, determining a protein's three-dimensional structure required time-consuming and expensive experimental techniques, often taking months or years per protein. AlphaFold2 compressed this process to minutes, and DeepMind released structural predictions for over 200 million proteins, effectively solving the structure prediction problem for the vast majority of known protein sequences.

The implications for drug discovery proved immediate and profound. By accurately predicting target protein structures, researchers can design drugs that specifically bind to these proteins. The AlphaFold2 structures were utilised to construct the first pocket library for all proteins in the human proteome through the CavitySpace database, which can be applied to identify novel targets for known drugs in drug repurposing.

Virtual ligand screening became dramatically more accessible. With predicted structures available for previously uncharacterised targets, researchers can computationally evaluate how small molecules or biological drugs might bind and identify promising candidates without extensive experimental screening. This accelerates early discovery and expands the druggable proteome to include targets that were previously intractable.

AlphaFold3, released subsequently, extended these capabilities to predict the structure and interactions of all life's molecules with unprecedented accuracy. The system achieves remarkable precision in predicting drug-like interactions, including protein-ligand binding and antibody-target protein interactions. Millions of researchers globally have used AlphaFold2 to make discoveries in areas including malaria vaccines, cancer treatments, and enzyme design.

However, AlphaFold doesn't solve drug discovery single-handedly. Knowing a protein's structure doesn't automatically reveal how to drug it effectively, what selectivity a drug molecule needs to avoid off-target effects, or how a compound will behave in complex in vivo environments. Structure is necessary but not sufficient.

Cautionary Tales and Realistic Expectations

The enthusiasm around AI in drug discovery must be tempered with realistic assessment. The technology is powerful but not infallible, and the path from computational prediction to approved medicine remains long and uncertain.

Consider that as of 2024, despite years of development and billions in investment, no AI-first drug has reached the market. The candidates advancing through clinical trials represent genuine progress, but they haven't yet crossed the ultimate threshold: demonstrating in large, well-controlled clinical trials that they are safe and effective enough to win regulatory approval.

A Nature article in 2023 warned that “AI's potential to accelerate drug discovery needs a reality check,” cautioning that the field risks overpromising and underdelivering. Previous waves of computational drug discovery enthusiasm, from structure-based design in the 1990s to systems biology in the 2000s, generated substantial hype but modest real-world impact.

The data quality problem represents a persistent challenge. Machine learning systems are only as good as their training data, and biological datasets often contain errors, biases, and gaps. Models trained on noisy data will perpetuate and potentially amplify these limitations.

The “black box” problem creates both scientific and regulatory concerns. Deep learning models make predictions through layers of mathematical transformations that can be difficult or impossible to interpret mechanistically. This opacity creates challenges for troubleshooting when predictions fail and for satisfying regulatory requirements for mechanistic understanding.

Integration challenges between AI teams and traditional pharmaceutical organisations also create friction. Drug discovery requires deep domain expertise in medicinal chemistry, pharmacology, toxicology, and clinical medicine. AI systems can augment but not replace this expertise. Organisations must successfully integrate computational and experimental teams, aligning incentives and workflows. This cultural integration proves harder than technical integration in many cases.

The capital intensity of drug development means that even dramatic improvements in early discovery efficiency may not transform overall economics as much as proponents hope. If AI compresses preclinical timelines from six years to two and improves Phase I success rates from 40 percent to 85 percent, clinical development from Phase II through approval still requires many years and hundreds of millions of pounds.

The Transformative Horizon

Despite caveats and challenges, the trajectory of AI in drug discovery points toward transformation rather than incremental change. The technology is still in early stages, analogous perhaps to the internet in the mid-1990s: clearly important, but with most applications and business models still to be developed.

Several technological frontiers promise to extend AI's impact. Multi-modal models that integrate diverse data types could capture biological complexity more comprehensively than current approaches. Active learning approaches, where AI systems guide experimental work by identifying the most informative next experiments, could accelerate iteration between computational and experimental phases.

The extension of AI into clinical development represents a largely untapped opportunity. Current systems focus primarily on preclinical discovery, but machine learning could also optimise trial design, identify suitable patients, predict which subpopulations will respond to therapy, and detect safety signals earlier. Recursion Pharmaceuticals is expanding AI focus to clinical trials, recognising that later pipeline stages offer substantial room for improvement.

Foundation models trained on massive biological datasets, analogous to large language models like GPT-4, may develop emergent capabilities that narrow AI systems lack. These models could potentially transfer learning across therapeutic areas, applying insights from oncology to inform neuroscience programmes.

The democratisation of AI tools could also accelerate progress. As platforms become more accessible, smaller biotechs and academic laboratories that lack substantial AI expertise could leverage the technology. Open-source models and datasets, such as AlphaFold's freely available protein structures, exemplify this democratising potential.

Regulatory adaptation will continue as agencies gain experience evaluating AI-discovered drugs. The frameworks emerging now will evolve as regulators develop institutional knowledge about validation standards and how to balance encouraging innovation with ensuring patient safety.

Perhaps most intriguingly, AI could expand the druggable proteome and enable entirely new therapeutic modalities. Many disease-relevant proteins have been considered “undruggable” because they lack obvious binding pockets for small molecules or prove difficult to target with conventional antibodies. AI systems that can design novel protein therapeutics, peptides, or other modalities tailored to these challenging targets might unlock therapeutic opportunities that were previously inaccessible.

The pharmaceutical industry stands at an inflection point. The early successes of AI in drug discovery are substantial enough to command attention and investment, whilst the remaining challenges are tractable enough to inspire confidence that solutions will emerge. The question is no longer whether AI will transform drug discovery but rather how quickly and completely that transformation will unfold.

For patients waiting for treatments for rare diseases, aggressive cancers, and other conditions with high unmet medical need, the answer matters enormously. If AI can reliably compress discovery timelines, improve success rates, and expand the range of treatable diseases, it represents far more than a technological curiosity. It becomes a tool for reducing suffering and extending lives.

The algorithms won't replace human researchers, but they're increasingly working alongside them as partners in the search for better medicines. And based on what's emerging from laboratories worldwide, that partnership is beginning to deliver on its considerable promise.

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Sources and References

1. Radical Ventures. “An AI-driven Breakthrough in De Novo Antibody Design.” https://radical.vc/an-ai-driven-breakthrough-in-de-novo-antibody-design/

2. Science/AAAS. “AI conjures up potential new antibody drugs in a matter of months.” https://www.science.org/content/article/ai-conjures-potential-new-antibody-drugs-matter-months

3. Frontiers in Drug Discovery. “AI-accelerated therapeutic antibody development: practical insights.” https://www.frontiersin.org/journals/drug-discovery/articles/10.3389/fddsv.2024.1447867/full

4. PubMed Central. “AI In Action: Redefining Drug Discovery and Development.” PMC11800368. https://pmc.ncbi.nlm.nih.gov/articles/PMC11800368/

5. ScienceDirect. “How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons.” https://www.sciencedirect.com/science/article/pii/S135964462400134X

6. BioSpace. “Biopharma AI Collaborations Driving Innovative Change in Drug Development.” https://www.biospace.com/article/biopharma-ai-collaborations-driving-innovative-change-in-drug-development-/

7. Morgan Stanley. “AI Drug Discovery: Leading to New Medicines.” https://www.morganstanley.com/ideas/ai-drug-discovery

8. FDA. “FDA Proposes Framework to Advance Credibility of AI Models Used for Drug and Biological Product Submissions.” January 2025. https://www.fda.gov/news-events/press-announcements/fda-proposes-framework-advance-credibility-ai-models-used-drug-and-biological-product-submissions

9. Fenwick & West LLP. “Unpacking AI-Assisted Drug Discovery Patents.” https://www.fenwick.com/insights/publications/unpacking-ai-assisted-drug-discovery-patents

10. Ropes & Gray LLP. “Patentability Risks Posed by AI in Drug Discovery.” October 2024. https://www.ropesgray.com/en/insights/alerts/2024/10/patentability-risks-posed-by-ai-in-drug-discovery

11. PR Newswire. “Insilico Received Positive Topline Results from Two Phase 1 Trials of ISM5411.” January 2025. https://www.prnewswire.com/news-releases/insilico-received-positive-topline-results-from-two-phase-1-trials-of-ism5411-new-drug-designed-using-generative-ai-for-the-treatment-of-inflammatory-bowel-disease-302344176.html

12. EurekAlert. “Insilico Medicine announces developmental candidate benchmarks and timelines.” 2024. https://www.eurekalert.org/news-releases/1073344

13. Clinical Trials Arena. “Insilico's AI drug enters Phase II IPF trial.” https://www.clinicaltrialsarena.com/news/insilico-medicine-ins018055-ai/

14. UKRI. “Exscientia: a clinical pipeline for AI-designed drug candidates.” https://www.ukri.org/who-we-are/how-we-are-doing/research-outcomes-and-impact/bbsrc/exscientia-a-clinical-pipeline-for-ai-designed-drug-candidates/

15. FierceBiotech. “AI drug hunter Exscientia chops down 'rapidly emerging pipeline' to focus on 2 main oncology programs.” https://www.fiercebiotech.com/biotech/ai-drug-hunter-exscientia-chops-down-rapidly-emerging-pipeline-focus-2-main-oncology

16. Recursion Pharmaceuticals. “Pioneering AI Drug Discovery.” https://www.recursion.com

17. BioPharma Dive. “AI specialist Recursion trims pipeline in latest shakeup.” https://www.biopharmadive.com/news/recursion-pipeline-cuts-first-quarter-earnings/747119/

18. Ardigen. “Where AI Meets Wet-Lab: A Smarter Path to Biologics Discovery Success.” https://ardigen.com/where-ai-meets-wet-lab-a-smarter-path-to-biologics-discovery-success/

19. GEN News. “AI in Drug Discovery: Trust, but Verify.” https://www.genengnews.com/topics/drug-discovery/ai-in-drug-discovery-trust-but-verify/

20. Nature. “AlphaFold2 and its applications in the fields of biology and medicine.” Signal Transduction and Targeted Therapy. https://www.nature.com/articles/s41392-023-01381-z

21. Google DeepMind Blog. “AlphaFold 3 predicts the structure and interactions of all of life's molecules.” https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/

22. Absci Corporation. “Absci Initiates IND-Enabling Studies for ABS-101.” February 2024. https://investors.absci.com/news-releases/news-release-details/absci-initiates-ind-enabling-studies-abs-101-potential-best

23. Absci Corporation. “Absci First to Create and Validate De Novo Antibodies with Zero-Shot Generative AI.” https://investors.absci.com/news-releases/news-release-details/absci-first-create-and-validate-de-novo-antibodies-zero-shot

24. Generate:Biomedicines. “Generative Biology.” https://generatebiomedicines.com/generative-biology

25. Business Wire. “Generate Biomedicines Uses AI to Create Proteins That Have Never Existed.” December 1, 2022. https://www.businesswire.com/news/home/20221201005267/en/Generate-Biomedicines-Uses-AI-to-Create-Proteins-That-Have-Never-Existed

26. Chemical & Engineering News. “Generate Biomedicines opens cryo-EM lab to feed data back into its protein design algorithms.” https://cen.acs.org/pharmaceuticals/drug-discovery/Generate-Biomedicines-opens-cryo-EM/101/i23

27. Nature. “AI's potential to accelerate drug discovery needs a reality check.” 2023. https://www.nature.com/articles/d41586-023-03172-6

28. ScienceDirect. “New Horizons in Therapeutic Antibody Discovery: Opportunities and Challenges versus Small-Molecule Therapeutics.” https://www.sciencedirect.com/science/article/pii/S2472555222072173

29. Nature Reviews Drug Discovery. “The company landscape for artificial intelligence in large-molecule drug discovery.” https://www.nature.com/articles/d41573-023-00139-0

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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

Piles of stones. So many piles of stones.

Oh, early morning hours— when did we fall to odds? You were the finest part of me, now turned traitor.

When I lie still in the dark, I’m not greeted by the gentle light that once woke the room— but a blackness darker still, one that swallows even starlight into dim memory.

Distraction and prayer are my only weapons against you. And they work— but only while I wield them. As sleep loosens its grip and I drift toward waking, they slip from my hands and you return, washing over me like a tide.

Damn you, darkness. Leave me be. Stop trying to snuff out my lights. And there are so many lights. Fields of my mind lit by torches, bonfires carried by the ones who love me, who worry for me. Yet your cold, slick flood rises again and I begin to drown in your shallow, merciless four inches of despair

Well then— do your damnedest, old foe. I am not finished.

Light will win.
The faithful always meet again. There is no timeline
where you take the final victory. Knock me down— I’ll rise again from dust and ash and start anew.

I will take your power. I will ride your lightning. I will reshape you— not as a lament, but as something ornate, moving, and beautiful.

Love always,
Charlie

Pluribus Episode 6: HDP

See 100 Word reviews of previous episodes here

Episode 5 cliffhanger revealed: Carol turned vlogger and documented the frozen, shrink-wrapped body parts that fuel the Others' Human-Derived Protein drink. The cannibalism is explained by the body, mouth, but not the brain, of John Cena.

Diabaté returns in a glorious scene. He informs Carol that she is not in the private chat.

Motivated by Carol’s video, Manousos in Paraguay sets off to find her, but runs into “Mom.”

Gilligan served the obvious while shrugging it off. No narrative-changing reveals, just practical explanations of the Hive’s moral code. He provides plot answers, leaving us with bigger philosophical questions.

Watch it.

#tv #Pluribus #SciFi #VinceGilligan #AppleTV #Television #100WordReview #Larrys100 #100DaysToOffload #socialmedia

Usually my mind is potent, l I’ll just go grab a string of pearls from there

Like a necklace

Which I show to everybody’s delight

My brain

It used to be full of thoughts

But now there is nothing there

No strings of pearls.

It’s just like the inside of an empty oil barrel

And

I have no thoughts on that fact

But

But

From where then, would someone might ask that: why is this state of mind then so beautifully (arguably) described?

Do I have more barrels than one or something?

There are chapters in Scripture that don’t just tell you what Jesus did—they tell you what Jesus is still doing.

Matthew 9 is one of them.

This isn’t a chapter filled with quiet theology or gentle parables. Matthew 9 is motion. It is urgency. It is compassion exploding through every barrier. It is power meeting pain, authority colliding with impossibility, and mercy rewriting the lives of people who believed their story was already finished.

When you walk through Matthew 9 slowly—line by line, moment by moment—you begin to feel something shift inside you. Because this chapter does not simply introduce you to Jesus the miracle-worker. It introduces you to Jesus the interrupter. Jesus the restorer. Jesus the one who steps into the middle of your chaos and says, “Get up. I’m not done with you.”

And if there is anything people need today, it’s this reminder: Jesus is not finished with you. Not with your past. Not with your healing. Not with your faith. Not with your future. Not with the parts of your story you haven’t told another soul.

Matthew 9 is the proof.

Let’s walk through it—slowly, deeply, personally—because this is not ancient history. This is a map for your own transformation.

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THE PARALYZED MAN — FOR EVERY PERSON WHO FEELS STUCK

Matthew opens the chapter with a shocking moment: a group of friends carries a paralyzed man to Jesus. Not gently. Not politely. Mark tells us they literally ripped open a roof to lower him down.

That’s what desperation looks like. That’s what faith looks like when you’ve run out of options.

But here is the part most people miss:

The man was paralyzed physically, but his friends refused to be paralyzed spiritually.

How many times have you felt stuck in life? Stuck in fear. Stuck in shame. Stuck in old patterns. Stuck in the belief that things will never change.

Jesus looks at this man and says something nobody expected:

“Take heart, son; your sins are forgiven.”

Jesus didn’t begin with the legs. He began with the heart.

Because Jesus always goes to the wound beneath the wound.

Yes, we want God to fix the job situation… fix the relationship… fix the finances… fix the anxiety…

But sometimes Jesus looks deeper and says, “Let Me heal the guilt you’ve been carrying. Let Me free the shame you’ve been hiding. Let Me restore what no one else can see.”

Then He says the words that echo across centuries:

“Get up.”

And he does.

This is the Jesus of Matthew 9— the Jesus who lifts you from places you didn’t believe you’d ever rise from again.

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THE TAX COLLECTOR — FOR EVERY PERSON WHO FEELS TOO BROKEN TO BE CHOSEN

Next, Jesus walks up to a tax collector named Matthew.

Everyone hated tax collectors. They were seen as greedy, corrupt, traitors to their own people.

If you asked the religious leaders who deserved God’s attention, Matthew wouldn't even make the list.

And Jesus says to him:

“Follow Me.”

Not, “Fix your life first.” Not, “Earn your way.” Not, “Prove that you’re worthy.”

Simply: “Follow Me.”

Jesus doesn’t recruit the impressive. He recruits the available. He chooses the unexpected. He calls the ones everyone else rejected.

That means He can use you—even the parts of you that you think disqualify you.

Because God doesn’t call the perfect. He perfects the called.

And Matthew does something world-changing:

He got up and followed Him.

Sometimes the holiest thing you can do… is just get up.

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THE QUESTION ABOUT FASTING — FOR EVERY PERSON WHO FEELS LIKE THEY DON’T FIT THE EXPECTATIONS OF RELIGION

The religious leaders show up again, bothered that Jesus' disciples aren’t fasting. They want to control the narrative. They want to police the spiritual experience.

They want Jesus to fit in their box.

Jesus answers with one of the most liberating truths in Scripture:

“No one pours new wine into old wineskins.”

Translation:

“You don’t get to shrink the Kingdom of God down to your expectations.”

If the Pharisees represent anything today, it’s the voices that tell you:

“You’re not holy enough.” “You’re not disciplined enough.” “You don’t look religious.” “You’re not doing it right.”

And Jesus says, “I’m not here to maintain old systems. I’m here to make all things new.”

There’s freedom in that. Freedom from religious pressure. Freedom from spiritual comparison. Freedom from trying to earn what God already gave as a gift.

Matthew 9 is Jesus telling you: “You don’t have to fit the mold. Just follow Me.”

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THE BLEEDING WOMAN — FOR EVERY PERSON HIDING A QUIET, LONELY PAIN

Then comes one of the most emotionally powerful moments in the entire Gospel.

A woman who has been bleeding for 12 years—twelve years of isolation, shame, exhaustion, and being labeled “unclean”—pushes through the crowd.

She doesn’t even try to get His attention. She doesn’t call His name. She simply says in her heart:

“If I can just touch His garment…”

Most people don’t understand what it’s like to carry a hidden battle. A private suffering. A wound that drains you silently—emotionally, spiritually, mentally, financially.

But Jesus sees what no one else sees.

The moment she touches Him, Jesus stops everything—even though He’s in the middle of rushing to help someone else.

He turns her direction.

He acknowledges her existence.

He honors her courage.

He speaks directly into the place where her fear and faith were wrestling:

“Take heart, daughter; your faith has made you well.”

He doesn’t call her “woman.” He calls her “daughter.”

The title she had never heard in twelve years. The identity her suffering had stolen. The relationship she thought was impossible.

This is what Jesus does: He restores what pain tried to erase.

The healing wasn’t just physical. It was personal. It was relational. It was emotional.

God does not simply fix wounds— He restores identity.

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THE DEAD GIRL — FOR EVERY PERSON WHO THINKS IT’S TOO LATE

While Jesus is still talking to the woman, word arrives: a young girl has died.

The mourners have already begun. The world has already declared the final verdict. The story seems closed.

But Jesus walks into the house and makes a declaration so bold it sounds offensive:

“The girl is not dead but asleep.”

Everyone laughs at Him.

Not because they’re cruel— but because the situation looked too final to imagine any other outcome.

Isn’t that what we do? When a relationship seems ruined… When a dream collapses… When hope feels gone… When life takes a turn so sharp you can’t breathe through it…

We assume finality. We assume God is late. We assume the story is over.

But Jesus does not speak from the view of circumstance. He speaks from the view of sovereignty.

He takes her by the hand— the hand of someone who was beyond human help— and she rises.

Here is the message: What looks dead to people is often only sleeping in God’s hands.

You may think it’s too late. But Jesus still walks into rooms where hope has flatlined… and breathes life again.

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THE TWO BLIND MEN — FOR EVERY PERSON PRAYING BUT NOT SEEING RESULTS YET

Then come two men who are blind. They cry out: “Have mercy on us, Son of David!”

But Jesus doesn’t heal them immediately. He takes them indoors—away from the crowd— and asks them a question that echoes through your own faith:

“Do you believe I am able to do this?”

Not “Do you believe I will?” Not “Do you believe you deserve it?” Not “Do you believe you’ve earned it?”

But simply: “Do you believe I am able?”

There are seasons when you pray… and nothing changes. You ask… and Heaven feels silent. You keep walking… and the darkness doesn’t lift.

But Jesus is forming a deeper question inside you: “Do you believe I am able even before you see it?”

Their answer was simple: “Yes, Lord.”

And their eyes were opened.

This is the pattern of Matthew 9: Not power for power’s sake… but restoration for trust’s sake.

Jesus wants relationship, not transactions.

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THE MUTE DEMON-POSSESSED MAN — FOR EVERY PERSON WHO LOST THEIR VOICE

Finally, a man who cannot speak is brought to Jesus.

This is symbolic for so many people today: trauma stole their voice shame silenced them fear muted them grief shut them down life broke something inside them that used to speak freely

Jesus casts out the demon, and the man speaks again.

He doesn’t just regain a voice— he regains identity, agency, dignity.

When God heals you, He doesn’t just remove the darkness. He restores the voice the darkness tried to take.

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THE HARVEST IS PLENTIFUL — FOR EVERY BELIEVER WHO FEELS OVERWHELMED

The chapter ends with something beautiful. Jesus looks at the crowds—not with frustration, not with judgment, not with disappointment. The Scripture says:

“He had compassion on them.”

Why? Because they were “harassed and helpless, like sheep without a shepherd.”

Jesus sees the brokenness of the world clearer than we do. He sees the confusion, the exhaustion, the spiritual hunger.

And His response is not discouragement— it is calling.

“The harvest is plentiful, but the laborers are few. Ask the Lord of the harvest to send out workers.”

In other words: “There is so much healing to do. So many hearts to restore. So many people who need hope. And I want you to be part of it.”

Not because you're perfect. Not because you're strong. Not because you're impressive.

But because healed people become healers. Restored people become restorers. Redeemed people become ambassadors of redemption.

Matthew 9 is the story of Jesus walking into every form of human suffering… and bringing transformation every single time.

You are living inside that same story today.

If you sit with Matthew 9 long enough, you begin to notice a pattern woven through every miracle, every conversation, every interruption:

Jesus moves toward the wounded. Jesus moves toward the forgotten. Jesus moves toward the impossible. Jesus moves toward the overlooked. Jesus moves toward the ones who think they don’t deserve Him.

And the more you read, the clearer it becomes:

Jesus is always moving toward you.

Not because you got it all together. Not because you pray perfectly. Not because your faith never shakes. Not because you’ve mastered spiritual discipline.

But because He knows the truth that you often forget:

You are the very reason He came.

Matthew 9 is not a chapter about people who had strong faith. It is a chapter about people who had strong need.

And Jesus never ran from need—He ran toward it.

Let’s bring this into real life. Into your life. Into the places you wish God would hurry up and fix.

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WHEN YOU FEEL PARALYZED BY LIFE

Maybe you’re like the paralyzed man— alive but not moving, breathing but not progressing, surviving but not thriving.

Maybe something in your life feels stuck: a mindset a habit a relationship a fear a disappointment a wound you’ve never told anybody about

Matthew 9 whispers this:

Bring your paralyzed places to Jesus. He still says “Get up.”

Healing isn’t always loud. Sometimes it begins with the smallest shift inside your spirit— a spark of hope, a breath of courage, a willingness to believe that the future does not have to look like the past.

You are not as stuck as you feel. Jesus is already standing over the places that paralyzed you, and one word from Him can restore what years tried to destroy.

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WHEN YOU FEEL DISQUALIFIED

If you’ve ever felt unworthy of God’s attention… If you’ve ever thought your past disqualifies you… If you’ve ever wondered why God would choose you when others seem more “spiritual”…

Stand next to Matthew the tax collector.

The religious world wrote him off.

Jesus wrote him into history.

That’s what grace does— it rewrites stories people gave up on.

Jesus is not intimidated by your past. He’s not shocked by your mistakes. He’s not analyzing your résumé before deciding if He wants you.

He looks at you the same way He looked at Matthew:

“Follow Me.”

Not a command. An invitation.

And everything changes the moment you say yes.

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WHEN YOU’RE EXHAUSTED BY RELIGIOUS EXPECTATIONS

There’s a reason Jesus said you can’t put new wine into old wineskins.

He wasn’t talking about wine. He was talking about life.

The Pharisees followed God with rules. Jesus calls you to follow God with relationship.

Religion says: “Earn it.” Jesus says: “Receive it.”

Religion says: “Behave right, then you belong.” Jesus says: “You belong—and that belonging will change you.”

Matthew 9 releases you from the prison of perfectionism. It frees you from spiritual anxiety. It reminds you that God’s presence is not a test to pass—it’s a gift to embrace.

You don’t have to achieve your way into God’s love. You only have to accept the invitation.

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WHEN YOU’RE HIDING PAIN NO ONE KNOWS ABOUT

The bleeding woman teaches us something tender and fierce:

You can bring Jesus the wound you don’t bring anyone else.

She didn’t walk up boldly. She didn’t make a speech. She didn’t approach Him with confidence.

She crawled. She whispered. She hoped.

And that was enough for Jesus to turn around.

Maybe you’ve been carrying a private heartbreak— the kind that sits under your smile, the kind no one asks about because you hide it well, the kind you’ve learned to live with even though it drains you daily.

Hear this:

Jesus turns toward quiet pain.

Even if you can only reach for the hem of His garment. Even if your prayer is barely a whisper. Even if you can’t explain the depth of your hurt.

He sees your reach. He hears your hope. He honors your courage.

And like the woman in Matthew 9, you will rise again—not just healed, but restored.

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WHEN IT FEELS TOO LATE

Some situations in life look like that little girl’s room: cold silent final

People assume it’s over. Your own mind tells you it’s finished. Your emotions start grieving what you think you’ll never get back.

But Jesus speaks a radical truth into funerals of hope:

“She is not dead but asleep.”

Translation:

“This looks final to you, but not to Me.”

There are dreams, callings, relationships, passions, and parts of your heart that you thought were dead.

Jesus calls them “asleep.”

In His hands, anything can rise again. Anything can be restored. Anything can be breathed back into life.

You serve a God who is not intimidated by impossibility. You serve a Savior who steps into graves and calls people forward. You serve a King whose timing is perfect even when it feels late.

Do not give up on what God has not declared finished.

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WHEN YOU’RE NOT SEEING ANSWERS YET

The two blind men cry out for mercy. Jesus waits. He doesn’t answer immediately. He brings them indoors, where faith is not shaped by visibility, applause, or emotion.

He asks: “Do you believe I am able to do this?”

That question is the furnace where real faith is forged.

Maybe you’ve been praying for something— direction healing breakthrough clarity strength provision peace— and it feels like nothing is happening.

But something is happening. God is forming your faith in the unseen.

Faith is not believing God will do it. Faith is believing God can—before you ever see the evidence.

And when Jesus touches your life in His timing, the scales will fall from your eyes and you’ll understand something profound:

Delay was never denial. Delay was preparation.

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WHEN LIFE HAS STOLEN YOUR VOICE

The man who couldn’t speak represents anyone who has been silenced— by trauma, by shame, by heartbreak, by discouragement, by the opinions of people, by seasons that crushed your spirit.

Jesus restores voices.

He restores confidence. He restores dignity. He restores the ability to speak truth, hope, and purpose into the world again.

If life has muted you, hear this with your heart:

Jesus is restoring your voice. Not just so you can speak— but so you can testify.

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WHEN THE WORLD LOOKS BROKEN

Matthew 9 ends with Jesus looking at crowds of hurting people.

Not criticizing. Not rolling His eyes. Not frustrated by their weakness.

The Scripture says He was moved with compassion.

Then He said something astonishing:

“The harvest is plentiful, but the workers are few.”

Meaning:

“There is so much healing to be done—and I want you in the middle of it.”

You are not just someone Jesus heals. You are someone Jesus sends.

Not because you’re strong. But because you know what it’s like to need Him.

The world doesn’t need perfect Christians. It needs healed ones. Restored ones. Compassionate ones. Christ-centered ones. People who have met Jesus in the middle of their own pain and now carry His hope to others.

That’s the real end of Matthew 9.

Not just transformation— but multiplication.

Jesus heals you so you can become part of His healing movement in the world.

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CONCLUSION — WHAT MATTHEW 9 MEANS FOR YOU TODAY

If Matthew 9 could speak directly to your life, it would say this:

You are not too stuck for Jesus. You are not too broken for Jesus. You are not too late for Jesus. You are not too quiet for Jesus. You are not too complicated for Jesus. You are not too far for Jesus.

Your story is not over. Your hope is not dead. Your faith is not empty. Your future is not ruined. Your calling is not canceled.

Every place of hurt— He can heal.

Every place of shame— He can restore.

Every place of impossibility— He can resurrect.

Every place where you feel small— He can speak identity.

Matthew 9 is not just a chapter you read. It is a chapter you live. A chapter that breathes inside you when everything feels impossible and God feels far away.

Jesus is not done moving in your life.

Not today. Not tomorrow. Not ever.

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#Jesus #Faith #BibleStudy #ChristianLiving #Hope #Inspiration #Motivation #Matthew9 #Healing #Miracles

I’m right now walking Hash, And I just have this Vague feeling about how I’m unhappy with my current life state. But I really want to remind myself that there aren’t necessarily big reasons to feel this way other than just the fact that this is what I’m used to in my comfortable state in my mind. But I also do have a lot of choice on perspective, if I choose to focus on the things where I feel good about my life then I will feel that way.

19 November 2025

Pink/moon (working title), or maybe Rudder: today's Oblique Strategies advised “infinitesimal gradations,” which is timely—this is a painting of the moon or sun in the London winter sky made with many thin layers of white tinted by various intensities of red and blue. Tried to make the difference in the tints as subtle as possible, George Tooker's embossed inkless intaglios in mind. This toward defamiliarizing and holding anew the scene hovering above that has become so familiar in the past three years. Following the details of sensation right now above all else, paying attention to their peaks and valleys, trying to relax into circling around their elusive core.

JOURNAL 5 décembre 2025

Je retourne dans ma tête mes entretiens de hier avec les psys. C’est vrai que je n’ai pas envie d'aller trouver l'explication. C’est vrai que je la connais, mais pour le moment je ne veux pas savoir. Je ne veux pas me dire ok c'est ça, parce que automatiquement je serais amenée à poser la question genre « est ce que je suis ce que je crois que je suis » et je ne veux pas poser cette question précisément, j'ai peur que la réponse soit négative. A me regarde et je crois qu’elle aussi est un peu inquiète de la réponse. Parce que si c’est négatif, alors je devrai remettre toute ma vie en question. ET ÇA ME FERAIT BIEN CHIER Je ne veux pas me poser de questions sur ma vie, c’est pour ça que j'ai peur de sauter. J’ai peut être tort, mais c’est existentiel, alors c’est un risque quand même.

Je sais bien que je le ferai et bientôt mais en attendant je vais me blottir dans les bras de A comme une petite fille un peu malade fermer mes yeux noirs de mer et plus penser à rien à rien du tout

#002269 – 15 de Agosto de 2025

Jorge Luis Borges elogiava a língua inglesa por ter, para muitas ideias, duas palavras: uma de origem germânica e outra de origem latina. Segundo ele, “regal” e “kingly” não expressam a mesma coisa. Noutros dos exemplos que dá, temos: “dark” e “obscure”, “holy spirit” e “holy ghost”.

A mim dava-me jeito que o português tivesse outra palavra para explorar (no sentido de procurar, investigar, indagar) que não tivesse o peso histórico do outro sentido da palavra, ligado ao capitalismo e ao colonialismo. Nenhum dos sinónimos que conheço me satisfaz e assim acabo por usar a palavra explorar mas amuado, desiludido com a minha falta de agilidade vocabular.

Klimat

And then there was Jane An honest Lebanese Had effects of war Due to the day He was elected And pretending the suffering was less than his return Returning responsibility- to the atom And a summons bemount And a pressure of before And an aneurysm in the Heavens- Exploded all space Fourteen Major Generals Signed an odious account On behalf of the General Who swayed them To play pick-up-sticks and go home To be off playing radar While planes began downing Over England Two honest affairs, And one above Turkey Was a precious drop of cargo From Heaven And in it contained light, And a bust of Summerman While onlookers vanished, At the beast

🐡 OpenBSD

How I’ve grown Layers of travel to each quantum bit In parity, a desk drawer Lighting chances to be fate, I love what I have found Nothing to censor and I believe- The surest bet is OpenBSD Waves of cool choices like time, Nothing to expend- Taking the reins that we will be together Horses drawn and the wheel ready I want on that USB The Romans were here and stole our code Ready-made stuff Thoroughly imbued what’s important Home for the living- Having bettered our world Join us.

Der Tag beginnt mit einer Tasse Tee. Eine frisch gekochte Kanne Kaffee folgt mir unauffällig ins Home-Office. Ich starte den Laptop, schalte die Kamera ein und entzünde ein Adventslicht. Der Bildschirmschoner der Hochschule leuchtet mir entgegen.

Um 8:00 Uhr geht es dann lost mit der virtuellen Lehre, dem Live-Tutorium “Inklusion”.

Ich öffne E-Mails und finde eine Bewertung der Studierenden, die mir ein Lächeln ins Gesicht zaubert. Ein Glitzermoment!

Das Tutorium geht los. Ich habe Wiederholungen vorbereitet. Wir starten mit einem Kahoot-Quiz. Dann planen die Studierenden Projekte zur Förderung der Inklusion in ihren Heimatstädten, gestalten Visionboards und beschreiben Whiteboards. Zwischendurch gibt es immer wieder Input und dann ist der letzte Termin in dieser Reihe zu Ende.

Ich besteige das Fahrrad und radel durch den herbstlichen Park Richtung Einkaufsmeile. Dabei probiere ich einen neuen Fahrradweg aus, der durch leuchtende Birkenstämme führt.

In der Vorderreihe ist Adventsmarkt. Zwischen den Ständen wärmen Feuerstellen, es duftet nach Holzkohle und Essen.

“Alles selbst gemacht!” erzählt die Verkäuferin hinter einem Stand mit vielen liebevoll gestalteten Weihnachtsartikeln stolz.

Ich radele weiter zum Meer. Liegt Schnee in der Luft? Luft 7 Grad, Wasser 4 Grad verrät die Informationstafel. Zu warm für Schnee.

Die Trelche wurden gerade erst wieder an der Nordermole aufgestellt. Sie waren zeitweise kopflos.

Nach einem kleinen Lunch geht es zurück ins Home-Office. Zwischendurch bleibt Zeit für das Klavier.

Um fünf Uhr nachmittags startet die letzte virtuelle Lehre in diesem Jahr. Eine Einheit von 90 Minuten, in der die Studierenden ihre Arbeit in ihren Praxisbetrieben reflektieren. Ein virtueller Adventskalender mit Mini-Impulsen bildet den Einstieg. Einige Themen werden vertieft. Die Wintermonate sind in der Sozialen Arbeit besonders herausfordernd, besonders die Arbeit mit Kindern und Jugendlichen in stationären Einrichtungen. Zum Abschluss laden Zukunftsfragen zu einem Ausblick in das neue Jahr ein. Es werden Glitzerfunken gestreut: Welcher kurze Moment zeigt dir, dass Du auf dem richtigen Weg bist? Und Fußspuren im Schnee hinterlassen: Was ist Dein ganz persönlicher Weg?

Der Abend klingt mit einem leckeren Essen, Wein und einem Weihnachtsfilm aus der Filmfriend Bibliothek aus.

___________________________________________________

Gerne mache ich wieder mit bei “Was machst Du eigentlich den ganzen Tag?” oder kurz #WMDEDGT.

Zu dieser Frage trifft sich der Freundeskreis des Tagebuchbloggen am 5. eines Monats in Frau Brüllens Blog. Danke dafür! Es macht viel Spaß!

Die Regeln zum Mitmachen sind einfach:

über den heutigen 5. Tag eines Monats tagebuchbloggen (ohne Werbung, ohne Geschwurbel) und verlinken.

#002268 – 14 de Agosto de 2025

Na maravilhosa série “Wayfarers” (tetralogia, até ao momento), Becky Chambers mostra que uma sensibilidade de política identitária não tem necessariamente de tornar a ficção estéril e irrelevante. Há autoras como a N. K. Jemisin que, tendo essa sensibilidade enquanto cidadãs, não a transportam para a linguagem dos seus livros. Já Chambers adopta os pronomes mais usuais, que incluem (além do feminino e do masculino) forma de referir alguém cujo género desconhecemos e também alguém cujo identidade de género não cabe numa lógica binária. As narrativas são fluídas, divertidas e exuberantes, como nas melhores space operas. A escrita é despretensiosa mas cintilante.

Ainda assim, é precisamente nestas questões de género que noto a maior fragilidade destes livros. Algo que é mais saliente ainda pelo facto impressionante e raro de estes romances não serem antropocêntricos. A maior parte das personagens são extraterrestres, nem sequer aparentados com os mamíferos do nosso planeta. Por isso é tão estranho ler as palavras homem, mulher, não-binário, para referir seres que não são humanos nem sequer se assemelham a humanos. Se até no nosso planeta há culturas que não fazem esta divisão dos sexos, por que motivo haveriam as espécies de uma galáxia inteira estar alinhadas nesta taxonomia?

Bem mais interessantes são experiências como a de Iain M. Banks, em “The Player of Games”, em que uma espécie de humanoides tem três sexos, todos necessários para a reprodução, ou o exemplo clássico de “The Left Hand of Darkness”, da Ursula K. Le Guin. É verdade que há umas décadas atrás, ao se pensar nestas questões se pensava sobretudo no sexo e pouco na expressão ou identidade de género. Mas sinto que em relação ao género ainda estamos no início da sua exploração pela ficção científica e num momento cultural em que não há sequer muita vontade de explorar, sendo o medo vigente imensamente paralisador da criatividade.

#002267 – 13 de Agosto de 2025

A vida não tem a obrigação de ser plausível, como a ficção. Nem vocação para ter sentido, como os enredos.

Lydia's Weekly Lifestyle blog is for today's African girl, so no subject is taboo. My purpose is to share things that may interest today's African girl.

This week's contributors: Lydia, Pépé Pépinière, Titi. This week's subjects: From Makola to the Boardroom: How to Slay Corporate Chic in Accra, Accra Fashion Week25 from 15th till 21st December, He is bored, Headache, constipation and UTIs, and How to spell Bofrot

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From Makola to the Boardroom: How to Slay Corporate Chic in Accra. Yesss! A playful, stylish, and proudly Accra-themed fashion — the kind that sparkles with personality, humor, and confidence while still sounding polished and fashion-savvy: Because you can buy fabric at dawn and close deals by noon. Accra women are a different kind of powerful. We know how to hustle at Makola, negotiate at Airport City, and still show up at work looking like a million cedis. Call it magic — or just good fashion sense. Here’s your guide to looking like you own the boardroom — with a little Makola flair on the side. The Fabric Hunt: Makola, But Make It Fashion Let’s be honest — Makola Market is not for the faint of heart, but it’s where style dreams are born. Between the buzzing stalls and endless rolls of Ankara, crepe, and linen, lies the beginning of every corporate slay. If you know your tailors and your fabrics, you can easily turn ₵100 worth of material into an outfit that screams executive presence. Pro tip: Stick with classic prints and neutral tones. Think deep blues, rich browns, and subtle golds — they say “promotion ready,” not “weekend wedding guest.” Tailor-made Confidence: In Accra, your tailor is practically your stylist. A well-cut suit or dress can change your whole aura. Forget the oversized blazers and stiff skirts — go for fit and flair. A cinched waist, structured shoulders, or a peplum hem can transform “office outfit” into “main character energy.” When your clothes fit well, you don’t just walk — you glide through the office corridors like it’s your runway (because it is). Accra Fashion Week25 from 15th till 21st December. It's 10 years since they started and it's going to be a big do with designers from 15 countries and models from Ethiopia, Ghana, Togo, Sierra Leone and others. Better get your tickets now, the big shows are on Saturday and Sunday 20st and 21nd. Or try to get free tickets through one of the models or designers.

He is bored. Happy together with hubby? Takes care of your everything and is ever so friendly and helpful and polite. But seems not really interested in you in the bed anymore. He sees every girl his car (with you inside) passes, in fact he looks at every woman he sees, even whilst sitting or walking next to you. What happened? This is not stress in the office, this is you. You are not exiting him anymore, whilst his sex drive is there all right. Be honest, have you been giving him the attention he wants? The compliments he is looking for? The challenges that light his fire? Maybe rather you are stressed, office, kids, money. But you'll get a lot more stressed if you don’t take care of him. So take the lead. Book a table at that restaurant nearby and dress for the waiter's eyes to pop out. Or wait for him in the bedroom when he gets home and call “ Honey I'm here”, dressed as when you were born, or even with some decorations on you. To create a situation like that may not be as simple as I write it here. But, very simply said, either you do something, or trouble will come. He'll find what he wants, but it may not be with you.

Headache, constipation and UTIs. I wrote earlier that lots of people drink far too little water, resulting in a permanent dehydrated status, often leading to very frequent, sometimes permanent headaches. And more. Constipation also often results, the stool becomes so dry that it simply does not want to move. This gives a bloated feeling but is also unhealthy. The body has decided to reject certain things, but rather you keep them in you, so some of these rejected, maybe poisonous things can still get absorbed. And there is UTI ( urinary tract infection). If you don't urinate regularly any infection which tries to creep up there does not get washed out and gets a full chance to install itself. And because your bladder lining is already irritated by the too dark urine the potential infection gets an additional chance to install itself. So drink. How much? About 0.03-0,04 liter per kg body weight. So if you weigh 60 kg you should get about 60 x 0.03 to 0.04 = 1.8-2.4 liter of water per day, 4-5 sachets. Some of that will come through food. And the colour of your urine should be very light white/yellow, not yellow/orange/brown. Can't afford 4 sachets per day? (4 sachets per day is about 700 GHS per year!). Nothing wrong with our tap water, GWC adds plenty chloride so we don't get cholera. And that's where most of the sachet manufacturers get their water anyway.

How to spell Bofrot. This is a good one to keep the conversation going during a dinner, and if you ask AI it will give you a good demo on how stupid AI really is. And these days if you google something it even puts a notice “thinking, thinking”, whilst in fact the algorithm is only searching through trillions of data, in the case for bofrot in vain. And not thinking. I've heard many versions on Bofrot, personally I like the ball float, copying the dough coming up when done. And a women near me is selling ballfloats so nice that now every afternoon there is a traffic jam there, and a guy opposite has started selling towels and trousers, a market is being formed right there on that busy street. Baflute?

Lydia...

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