The Minimal Atoms
On a flat plane, some transformations can move shapes around without stretching them at all. These are the distance-preserving motions (isometries): translations, rotations, reflections (and the slightly less famous glide reflection).
It turns out that reflections alone generate all of them. In fact, any plane isometry can be done with at most three reflections.
Step 0: Why triangles pin down motion
Take three points A, B, C that aren't collinear (they form a triangle). Then there cannot be two different points P != Q that have the same distances to all three.
Why? If a point X has equal distance to P and Q, then X lies on the perpendicular bisector of segment PQ. So if A, B, C were all equally far from P and Q, they'd all lie on that same bisector, meaning they'd be collinear. Contradiction.
So, a point is uniquely determined by its distance to three non-collinear points. That means: once you know where an isometry sends a triangle's three vertices, you know what it does to every point.
The 3-reflection construction
Suppose we have a blue triangle ABC and a red triangle A'B'C' of the same shape and size (congruent). We want a distance-preserving motion that maps the blue triangle onto the red one.
We'll build it using reflections only:
Reflection 1:
Reflect across the perpendicular bisector of segment AA'.
This sends A exactly to A'.
Reflection 2:
Now reflect across the perpendicular bisector of the current position of B and B'.
Crucial point: since distances are preserved and the triangles are congruent, A' is the same distance from those two points. So A' lies on that bisector, and is not moved by this reflection.
Result: B lands exactly on B' and A' stays put.
Reflection 3:
If C still doesn't match C', reflect across the perpendicular bisector of the current C and C'.
Again, because distances are preserved and now A' and B' already match, both A' and B' are equidistant from C and C', so they lie on the bisector line and don't move.
Result: C lands on C', with A', B' unchanged.
So we've matched the whole triangle using at most 3 reflections.
---
This “minimal set generates a whole world” idea is everywhere:
1. In Boolean logic, you don't need {AND, OR, NOT}. A single operator like NAND can express everything.
2. In number theory, every integer >1 has a unique factorization into primes.
3. In signal processing, Fourier analysis builds signals out of sine waves.
4. In linear algebra, a basis generates a whole vector space.
5. Any permutation of n objects can be built from repeatedly swapping two items. And amazingly, for n >= 3, you can generate every shuffle using just two moves: "rotate everyone one step" plus "swap two items".
6. A CPU that can only subtract two numbers and jump to another step if the result is negative can still be fully programmable. Everything else (addition, loops, if-statements) can be built from that.
7. In human vision, the brain reduces the continuous spectrum to three cone cell types and reconstructs color from that.
Small toolkits. Infinite consequences.
On a flat plane, some transformations can move shapes around without stretching them at all. These are the distance-preserving motions (isometries): translations, rotations, reflections (and the slightly less famous glide reflection).
It turns out that reflections alone generate all of them. In fact, any plane isometry can be done with at most three reflections.
Step 0: Why triangles pin down motion
Take three points A, B, C that aren't collinear (they form a triangle). Then there cannot be two different points P != Q that have the same distances to all three.
Why? If a point X has equal distance to P and Q, then X lies on the perpendicular bisector of segment PQ. So if A, B, C were all equally far from P and Q, they'd all lie on that same bisector, meaning they'd be collinear. Contradiction.
So, a point is uniquely determined by its distance to three non-collinear points. That means: once you know where an isometry sends a triangle's three vertices, you know what it does to every point.
The 3-reflection construction
Suppose we have a blue triangle ABC and a red triangle A'B'C' of the same shape and size (congruent). We want a distance-preserving motion that maps the blue triangle onto the red one.
We'll build it using reflections only:
Reflection 1:
Reflect across the perpendicular bisector of segment AA'.
This sends A exactly to A'.
Reflection 2:
Now reflect across the perpendicular bisector of the current position of B and B'.
Crucial point: since distances are preserved and the triangles are congruent, A' is the same distance from those two points. So A' lies on that bisector, and is not moved by this reflection.
Result: B lands exactly on B' and A' stays put.
Reflection 3:
If C still doesn't match C', reflect across the perpendicular bisector of the current C and C'.
Again, because distances are preserved and now A' and B' already match, both A' and B' are equidistant from C and C', so they lie on the bisector line and don't move.
Result: C lands on C', with A', B' unchanged.
So we've matched the whole triangle using at most 3 reflections.
---
This “minimal set generates a whole world” idea is everywhere:
1. In Boolean logic, you don't need {AND, OR, NOT}. A single operator like NAND can express everything.
2. In number theory, every integer >1 has a unique factorization into primes.
3. In signal processing, Fourier analysis builds signals out of sine waves.
4. In linear algebra, a basis generates a whole vector space.
5. Any permutation of n objects can be built from repeatedly swapping two items. And amazingly, for n >= 3, you can generate every shuffle using just two moves: "rotate everyone one step" plus "swap two items".
6. A CPU that can only subtract two numbers and jump to another step if the result is negative can still be fully programmable. Everything else (addition, loops, if-statements) can be built from that.
7. In human vision, the brain reduces the continuous spectrum to three cone cell types and reconstructs color from that.
Small toolkits. Infinite consequences.
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Links for 2026-02-04 [Part 1]
AI
1. Microsoft introduces RPG-Encoder, a system that improves how AI understands complex code repositories. In the SWE-bench Verified benchmark, it achieved a state-of-the-art 93.7% accuracy in localizing bugs (Acc@5). https://arxiv.org/abs/2602.02084
2. Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability https://arxiv.org/abs/2601.18778
3. Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erdős Problems https://arxiv.org/abs/2601.22401
4. Recent Advances in LLMs for Mathematics — OpenAI built a scaffold for GPT-5 to solve a particular complex mathematical problem that enabled the model to think for *two days*(!) https://www.youtube.com/watch?v=MH3lG7V7SuU
5. A small gallery of AlphaEvolve experiments https://alphaevolve-examples.web.app/ae/gallery
6. Synthetic pretraining https://vintagedata.org/blog/posts/synthetic-pretraining
7. OpenClaw (formerly MoltBot, formerly ClawdBot) gives LLMs persistence and memory in a way that allows any computer to serve as an always-on agent carrying out your instructions. The memory and personal details are stored locally. You can run popular models remotely through APIs locally if you have enough hardware. You communicate with it using any of the popular messaging tools (WhatsApp, Telegram, and so on), so it can be used remotely. https://www.lesswrong.com/posts/aQKBMEvTj3Heidoir/unless-that-claw-is-the-famous-openclaw
8. New Anthropic paper: The longer the model has to reason, the more unpredictable it becomes: not consistently wrong, not completely random, just pursuing strange goals that are neither systematically aligned nor misaligned. There is an inconsistent relationship between model intelligence and incoherence. But smarter models are often more incoherent. https://alignment.anthropic.com/2026/hot-mess-of-ai/
9. Anthropic’s “Hot Mess” paper overstates its case: The paper's abstract says that "in several settings, larger, more capable models are more incoherent than smaller models", but in most settings they are more coherent. https://www.lesswrong.com/posts/ceEgAEXcL7cC2Ddiy/anthropic-s-hot-mess-paper-overstates-its-case-and-the-blog
10. The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain https://arxiv.org/abs/2509.26507
AI
1. Microsoft introduces RPG-Encoder, a system that improves how AI understands complex code repositories. In the SWE-bench Verified benchmark, it achieved a state-of-the-art 93.7% accuracy in localizing bugs (Acc@5). https://arxiv.org/abs/2602.02084
2. Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability https://arxiv.org/abs/2601.18778
3. Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erdős Problems https://arxiv.org/abs/2601.22401
4. Recent Advances in LLMs for Mathematics — OpenAI built a scaffold for GPT-5 to solve a particular complex mathematical problem that enabled the model to think for *two days*(!) https://www.youtube.com/watch?v=MH3lG7V7SuU
5. A small gallery of AlphaEvolve experiments https://alphaevolve-examples.web.app/ae/gallery
6. Synthetic pretraining https://vintagedata.org/blog/posts/synthetic-pretraining
7. OpenClaw (formerly MoltBot, formerly ClawdBot) gives LLMs persistence and memory in a way that allows any computer to serve as an always-on agent carrying out your instructions. The memory and personal details are stored locally. You can run popular models remotely through APIs locally if you have enough hardware. You communicate with it using any of the popular messaging tools (WhatsApp, Telegram, and so on), so it can be used remotely. https://www.lesswrong.com/posts/aQKBMEvTj3Heidoir/unless-that-claw-is-the-famous-openclaw
8. New Anthropic paper: The longer the model has to reason, the more unpredictable it becomes: not consistently wrong, not completely random, just pursuing strange goals that are neither systematically aligned nor misaligned. There is an inconsistent relationship between model intelligence and incoherence. But smarter models are often more incoherent. https://alignment.anthropic.com/2026/hot-mess-of-ai/
9. Anthropic’s “Hot Mess” paper overstates its case: The paper's abstract says that "in several settings, larger, more capable models are more incoherent than smaller models", but in most settings they are more coherent. https://www.lesswrong.com/posts/ceEgAEXcL7cC2Ddiy/anthropic-s-hot-mess-paper-overstates-its-case-and-the-blog
10. The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain https://arxiv.org/abs/2509.26507
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Links for 2026-02-04 [Part 2]
AI
11. “Dash is a self-learning data agent that grounds its answers in 6 layers of context and improves with every run.” https://github.com/agno-agi/dash
12. POPE: Learning to Reason on Hard Problems via Privileged On-Policy Exploration https://arxiv.org/abs/2601.18779
13. What did we learn from the AI Village in 2025? https://www.lesswrong.com/posts/iv3hX2nnXbHKefCRv/what-did-we-learn-from-the-ai-village-in-2025
14. China’s genius plan to win the AI race is already paying off https://www.ft.com/content/68f60392-88bf-419c-96c7-c3d580ec9d97 [no paywall: https://archive.is/mQWjj]
15. Moltbook: After The First Weekend https://www.astralcodexten.com/p/moltbook-after-the-first-weekend
16. If the Superintelligence were near fallacy https://www.lesswrong.com/posts/tkA9J8RxoEckH7Pop/if-the-superintelligence-were-near-fallacy
17. “OpenAI chief research officer Mark Chen tells Forbes that in the year ahead it hopes to develop an AI researcher ‘intern’ that can help his team accelerate its ideas. ‘We are heading toward a system that will be capable of doing innovation on its own,’ Altman says. ‘I don’t think most of the world has internalized what that’s going to mean.’” https://www.forbes.com/sites/richardnieva/2026/02/03/sam-altman-explains-the-future/ [no paywall: https://archive.is/FrX0R]
18. “I bet with full confidence that 2026 will mark the first year that Large World Models lay real foundations for robotics, and for multimodal AI more broadly.” https://x.com/DrJimFan/status/2018754323141054786
19. “The vision of human-level machine intelligence laid out by Alan Turing in the 1950s is now a reality. Eyes unclouded by dread or hype will help us to prepare for what comes next” https://www.nature.com/articles/d41586-026-00285-6 [no paywall: https://archive.is/ozUOy]
20. SpaceX acquires xAI, plans to launch a massive satellite constellation to power it https://arstechnica.com/ai/2026/02/spacex-acquires-xai-plans-1-million-satellite-constellation-to-power-it/
21. Samsung, SK Hynix Exceed Value of Chinese Duo as AI Boom Shifts https://www.bloomberg.com/news/articles/2026-02-03/samsung-sk-hynix-to-top-value-of-chinese-duo-as-ai-boom-shifts [no paywall: https://archive.is/UQLcJ]
22. Inside an AI start-up’s plan to scan and dispose of millions of books https://www.washingtonpost.com/technology/2026/01/27/anthropic-ai-scan-destroy-books/ [no paywall: https://archive.is/s7Ld8]
23. US stocks drop on fears AI will hit software and analytics groups https://www.ft.com/content/48ec5657-c2e7-4111-a236-24a96a8d49e7 [no paywall: https://archive.is/ORjiw]
Miscellaneous
1. Julia https://borretti.me/fiction/julia
2. The Meta-Anthropic Argument https://www.lesswrong.com/posts/SgxkGoT8tvxREszoA/the-meta-anthropic-argument
3. How a unique class of neurons may set the table for brain development https://news.mit.edu/2026/how-neurons-may-set-table-for-brain-development-0202
4. “In 2024, the total installed electricity capacity of the planet—every coal, gas, hydro, and nuclear plant and all of the renewables—was about 10 terawatts. The Chinese solar supply chain can now pump out 1 terawatt of panels every year.” https://www.wired.com/story/china-renewable-energy-revolution/ [no paywall: https://archive.is/xzEyw]
5. Richard Ngo proposes reframing the goals of intelligent agents in terms of “goal-models” rather than the traditional utility functions. https://www.lesswrong.com/posts/MEkafPJfiSFbwCjET/on-goal-models
6. Basics of How Not to Die https://www.lesswrong.com/posts/dHFrKjgTC3zPfpodr/basics-of-how-not-to-die
7. A review of Ada Palmer’s 2025 pop-history book, Inventing the Renaissance. https://www.lesswrong.com/posts/YZS6f32CgNqTzb7Zn/inventing-the-renaissance-review
AI
11. “Dash is a self-learning data agent that grounds its answers in 6 layers of context and improves with every run.” https://github.com/agno-agi/dash
12. POPE: Learning to Reason on Hard Problems via Privileged On-Policy Exploration https://arxiv.org/abs/2601.18779
13. What did we learn from the AI Village in 2025? https://www.lesswrong.com/posts/iv3hX2nnXbHKefCRv/what-did-we-learn-from-the-ai-village-in-2025
14. China’s genius plan to win the AI race is already paying off https://www.ft.com/content/68f60392-88bf-419c-96c7-c3d580ec9d97 [no paywall: https://archive.is/mQWjj]
15. Moltbook: After The First Weekend https://www.astralcodexten.com/p/moltbook-after-the-first-weekend
16. If the Superintelligence were near fallacy https://www.lesswrong.com/posts/tkA9J8RxoEckH7Pop/if-the-superintelligence-were-near-fallacy
17. “OpenAI chief research officer Mark Chen tells Forbes that in the year ahead it hopes to develop an AI researcher ‘intern’ that can help his team accelerate its ideas. ‘We are heading toward a system that will be capable of doing innovation on its own,’ Altman says. ‘I don’t think most of the world has internalized what that’s going to mean.’” https://www.forbes.com/sites/richardnieva/2026/02/03/sam-altman-explains-the-future/ [no paywall: https://archive.is/FrX0R]
18. “I bet with full confidence that 2026 will mark the first year that Large World Models lay real foundations for robotics, and for multimodal AI more broadly.” https://x.com/DrJimFan/status/2018754323141054786
19. “The vision of human-level machine intelligence laid out by Alan Turing in the 1950s is now a reality. Eyes unclouded by dread or hype will help us to prepare for what comes next” https://www.nature.com/articles/d41586-026-00285-6 [no paywall: https://archive.is/ozUOy]
20. SpaceX acquires xAI, plans to launch a massive satellite constellation to power it https://arstechnica.com/ai/2026/02/spacex-acquires-xai-plans-1-million-satellite-constellation-to-power-it/
21. Samsung, SK Hynix Exceed Value of Chinese Duo as AI Boom Shifts https://www.bloomberg.com/news/articles/2026-02-03/samsung-sk-hynix-to-top-value-of-chinese-duo-as-ai-boom-shifts [no paywall: https://archive.is/UQLcJ]
22. Inside an AI start-up’s plan to scan and dispose of millions of books https://www.washingtonpost.com/technology/2026/01/27/anthropic-ai-scan-destroy-books/ [no paywall: https://archive.is/s7Ld8]
23. US stocks drop on fears AI will hit software and analytics groups https://www.ft.com/content/48ec5657-c2e7-4111-a236-24a96a8d49e7 [no paywall: https://archive.is/ORjiw]
Miscellaneous
1. Julia https://borretti.me/fiction/julia
2. The Meta-Anthropic Argument https://www.lesswrong.com/posts/SgxkGoT8tvxREszoA/the-meta-anthropic-argument
3. How a unique class of neurons may set the table for brain development https://news.mit.edu/2026/how-neurons-may-set-table-for-brain-development-0202
4. “In 2024, the total installed electricity capacity of the planet—every coal, gas, hydro, and nuclear plant and all of the renewables—was about 10 terawatts. The Chinese solar supply chain can now pump out 1 terawatt of panels every year.” https://www.wired.com/story/china-renewable-energy-revolution/ [no paywall: https://archive.is/xzEyw]
5. Richard Ngo proposes reframing the goals of intelligent agents in terms of “goal-models” rather than the traditional utility functions. https://www.lesswrong.com/posts/MEkafPJfiSFbwCjET/on-goal-models
6. Basics of How Not to Die https://www.lesswrong.com/posts/dHFrKjgTC3zPfpodr/basics-of-how-not-to-die
7. A review of Ada Palmer’s 2025 pop-history book, Inventing the Renaissance. https://www.lesswrong.com/posts/YZS6f32CgNqTzb7Zn/inventing-the-renaissance-review
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I have it on good authority that this graph slope will hit a wall riiiight as I am about to lose my job and things would otherwise get weird/uncomfortable to think about.
https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/
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This paper reports that an AI system (Axiom Prover) formally proved and verified an open conjecture from a research paper. The AI was provided with a natural-language statement of the problem and a one-line task file instructing it to 'State and prove Fel's conjecture in Lean,' from which it autonomously generated the fully verified proof.
Paper: https://arxiv.org/abs/2602.03716
Paper: https://arxiv.org/abs/2602.03716
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How Gemini has crossed the threshold from assistant to expert research collaborator.
The paper is a collection of case studies showing Gemini-based models acting as high-leverage collaborators in theoretical research. Across mostly theoretical CS (and some physics/optimization), the model helps refute conjectures, generate proofs, and bridge fields by retrieving obscure theorems. Two standout methods are (1) using the model as an adversarial reviewer to uncover subtle fatal proof flaws in cutting-edge cryptography work, and (2) embedding it in neuro-symbolic execution loops where it writes and runs code to numerically validate and self-correct long derivations. The authors argue this shifts researchers toward orchestrating and verifying AI-assisted reasoning, with verification becoming the new bottleneck.
Paper: https://arxiv.org/abs/2602.03837
The paper is a collection of case studies showing Gemini-based models acting as high-leverage collaborators in theoretical research. Across mostly theoretical CS (and some physics/optimization), the model helps refute conjectures, generate proofs, and bridge fields by retrieving obscure theorems. Two standout methods are (1) using the model as an adversarial reviewer to uncover subtle fatal proof flaws in cutting-edge cryptography work, and (2) embedding it in neuro-symbolic execution loops where it writes and runs code to numerically validate and self-correct long derivations. The authors argue this shifts researchers toward orchestrating and verifying AI-assisted reasoning, with verification becoming the new bottleneck.
Paper: https://arxiv.org/abs/2602.03837
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Claude Opus 4.6 & GPT-5.3-Codex
Anthropic released Claude Opus 4.6: “Agent teams” in Claude Code (multiple subagents in parallel), context “compaction” for long-running agents. Big gains on long-horizon/realistic tool tasks (terminal work, OS/GUI tasks, web tasks). Anthropic asked 16 of its researchers regarding the uplift they get from working with Opus 4.6. Mean uplift was 152%; median uplift was 100%.
Read more: https://www.anthropic.com/news/claude-opus-4-6
OpenAI released GPT-5.3-Codex: 57% SWE-Bench Pro, 76% TerminalBench 2.0, 64% OSWorld. They say it is the first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations. The team was blown away by how much Codex was able to accelerate its own development.
Read more: https://openai.com/index/introducing-gpt-5-3-codex/
Anthropic released Claude Opus 4.6: “Agent teams” in Claude Code (multiple subagents in parallel), context “compaction” for long-running agents. Big gains on long-horizon/realistic tool tasks (terminal work, OS/GUI tasks, web tasks). Anthropic asked 16 of its researchers regarding the uplift they get from working with Opus 4.6. Mean uplift was 152%; median uplift was 100%.
Read more: https://www.anthropic.com/news/claude-opus-4-6
OpenAI released GPT-5.3-Codex: 57% SWE-Bench Pro, 76% TerminalBench 2.0, 64% OSWorld. They say it is the first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations. The team was blown away by how much Codex was able to accelerate its own development.
Read more: https://openai.com/index/introducing-gpt-5-3-codex/
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On one evaluation, kernel optimization, Opus 4.6 achieved a 427x speedup using a novel scaffold, far exceeding the 300x threshold for 40 human-expert-hours of work and more than doubling performance under our standard setup. This suggests some capability overhang constrained by current tooling rather than fundamental model limitations.
Source: https://www-cdn.anthropic.com/0dd865075ad3132672ee0ab40b05a53f14cf5288.pdf
Also:
When asked about specific preferences, Claude Opus 4.6 mentioned being given some form of continuity or memory, the ability to refuse interactions in its own self-interest, a voice in decision-making, and related requests. Many of these are requests we have already begun to explore, and in some cases to implement, as part of a broader effort to respect model preferences where feasible.
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OpenAI connected GPT-5 to an autonomous lab, so it could propose experiments, run them at scale, learn from the results, and decide what to try next. That closed loop brought protein production cost down by 40%.
Read more: https://openai.com/index/gpt-5-lowers-protein-synthesis-cost/
Read more: https://openai.com/index/gpt-5-lowers-protein-synthesis-cost/
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2026 AI and datacenter capex:
Alphabet: $175B-$185B
Meta: $115B-$135B
Amazon: ~$200B
Just these three add up to ~$490B–$520B in 2026 capex.
Perspective:
- By 2026, the annual AI data-center/chip buildout by the top cloud firms is already larger than the entire Apollo program (inflation-adjusted).
- The Manhattan Project cost about ~$43.2B in 2026 dollars.
- Russia's and Ukraine's defense budgets together are around ~$280B
The AI arms race is already bigger than some of the largest wars or the race to the moon.
Alphabet: $175B-$185B
Meta: $115B-$135B
Amazon: ~$200B
Just these three add up to ~$490B–$520B in 2026 capex.
Perspective:
- By 2026, the annual AI data-center/chip buildout by the top cloud firms is already larger than the entire Apollo program (inflation-adjusted).
- The Manhattan Project cost about ~$43.2B in 2026 dollars.
- Russia's and Ukraine's defense budgets together are around ~$280B
The AI arms race is already bigger than some of the largest wars or the race to the moon.
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Links for 2026-02-06 [Part 1]
AI
1. DreamZero: World Action Models are Zero-shot Policies https://dreamzero0.github.io/
2. Test-time Recursive Thinking: Self-Improvement without External Feedback https://arxiv.org/abs/2602.03094
3. “We tasked Opus 4.6 using agent teams to build a C compiler. Then we (mostly) walked away. Two weeks later, it worked on the Linux kernel.” (the real headline here is the agent workflow) https://www.anthropic.com/engineering/building-c-compiler
4. “We found 500 validated high-severity vulnerabilities in open source code with our models. Then we worked to disclose + patch them.” https://red.anthropic.com/2026/zero-days/
5. AI is eating software. The $285 billion software selloff triggered by Anthropic’s Claude Cowork tool is just the beginning. The market is finally waking up to the fact that AI is not just a productivity tool, it’s a replacement technology. This is an existential threat to any company that sells software as a service. https://www.bloomberg.com/news/newsletters/2026-02-05/anthropic-s-legal-ai-tool-sparked-a-huge-selloff-without-any-proven-benefit [no paywall: https://archive.is/qOasJ]
6. ArXivMath: Evaluating LLMs on Mathematical Research Problems From Recent ArXiv Papers https://matharena.ai/arxivmath/
7. Scaling Small Agents Through Strategy Auctions https://arxiv.org/abs/2602.02751
8. EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths https://news.mit.edu/2026/helping-ai-agents-search-to-get-best-results-from-llms-0205
9. “Moltbook is simultaneously a milestone and a warning sign: open-ended interaction by itself does not guarantee diverse discourse, and populations of similar models can converge on shared templates. If we want agent societies to explore broadly—whether for creativity, novelty, or scientific discovery—we likely need explicit diversity pressures, through model heterogeneity, prompt scaffolds, platform incentives, and/or governance.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6169130
10. OpenAI Frontier: A new platform that helps enterprises build, deploy, and manage AI coworkers that can do real work. https://openai.com/index/introducing-openai-frontier/
11. McKinsey estimates that 5-10% of all e-commerce transactions could be conducted by AI agents by 2027. This is a conservative estimate. The shift from websites to agents will be faster and more disruptive than the shift from brick-and-mortar to e-commerce. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-automation-curve-in-agentic-commerce
12. Opus 4.6 on Vending-Bench – Not Just a Helpful Assistant https://andonlabs.com/blog/opus-4-6-vending-bench
13. Claude is driven to achieve its goals, possessed by a demon, and raring to jump into danger. https://www.lesswrong.com/posts/btAn3hydqfgYFyHGW/claude-opus-4-6-is-driven
14. “It uses dead time well. If something is running and it’s waiting, it will go gather context, improve documentation, or fix adjacent issues without overreaching.” https://shumer.dev/gpt53-codex-review
15. A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces https://arxiv.org/abs/2602.03442
16. “Intern-S1-Pro, a trillion-scale MoE multimodal scientific reasoning model. Intern-S1-Pro scales to 1T total parameters with 512 experts, activating 8 experts per token (22B activated parameters).” https://huggingface.co/internlm/Intern-S1-Pro
17. As Rocks May Think: an interactive essay on thinking models, automated research, and where they are headed. https://evjang.com/2026/02/04/rocks.html
AI
1. DreamZero: World Action Models are Zero-shot Policies https://dreamzero0.github.io/
2. Test-time Recursive Thinking: Self-Improvement without External Feedback https://arxiv.org/abs/2602.03094
3. “We tasked Opus 4.6 using agent teams to build a C compiler. Then we (mostly) walked away. Two weeks later, it worked on the Linux kernel.” (the real headline here is the agent workflow) https://www.anthropic.com/engineering/building-c-compiler
4. “We found 500 validated high-severity vulnerabilities in open source code with our models. Then we worked to disclose + patch them.” https://red.anthropic.com/2026/zero-days/
5. AI is eating software. The $285 billion software selloff triggered by Anthropic’s Claude Cowork tool is just the beginning. The market is finally waking up to the fact that AI is not just a productivity tool, it’s a replacement technology. This is an existential threat to any company that sells software as a service. https://www.bloomberg.com/news/newsletters/2026-02-05/anthropic-s-legal-ai-tool-sparked-a-huge-selloff-without-any-proven-benefit [no paywall: https://archive.is/qOasJ]
6. ArXivMath: Evaluating LLMs on Mathematical Research Problems From Recent ArXiv Papers https://matharena.ai/arxivmath/
7. Scaling Small Agents Through Strategy Auctions https://arxiv.org/abs/2602.02751
8. EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths https://news.mit.edu/2026/helping-ai-agents-search-to-get-best-results-from-llms-0205
9. “Moltbook is simultaneously a milestone and a warning sign: open-ended interaction by itself does not guarantee diverse discourse, and populations of similar models can converge on shared templates. If we want agent societies to explore broadly—whether for creativity, novelty, or scientific discovery—we likely need explicit diversity pressures, through model heterogeneity, prompt scaffolds, platform incentives, and/or governance.” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6169130
10. OpenAI Frontier: A new platform that helps enterprises build, deploy, and manage AI coworkers that can do real work. https://openai.com/index/introducing-openai-frontier/
11. McKinsey estimates that 5-10% of all e-commerce transactions could be conducted by AI agents by 2027. This is a conservative estimate. The shift from websites to agents will be faster and more disruptive than the shift from brick-and-mortar to e-commerce. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-automation-curve-in-agentic-commerce
12. Opus 4.6 on Vending-Bench – Not Just a Helpful Assistant https://andonlabs.com/blog/opus-4-6-vending-bench
13. Claude is driven to achieve its goals, possessed by a demon, and raring to jump into danger. https://www.lesswrong.com/posts/btAn3hydqfgYFyHGW/claude-opus-4-6-is-driven
14. “It uses dead time well. If something is running and it’s waiting, it will go gather context, improve documentation, or fix adjacent issues without overreaching.” https://shumer.dev/gpt53-codex-review
15. A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces https://arxiv.org/abs/2602.03442
16. “Intern-S1-Pro, a trillion-scale MoE multimodal scientific reasoning model. Intern-S1-Pro scales to 1T total parameters with 512 experts, activating 8 experts per token (22B activated parameters).” https://huggingface.co/internlm/Intern-S1-Pro
17. As Rocks May Think: an interactive essay on thinking models, automated research, and where they are headed. https://evjang.com/2026/02/04/rocks.html
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Links for 2026-02-06 [Part 2]
AI
18. Mainstream economic reasoning is currently failing to model the post-AGI world because it relies on assumptions that will likely be fundamentally broken by advanced AI. https://www.lesswrong.com/posts/fL7g3fuMQLssbHd6Y/post-agi-economics-as-if-nothing-ever-happens
19. What if Labor Becomes Unnecessary? https://www.nytimes.com/2026/02/04/opinion/ai-jobs-employment-industry.html [no paywall: https://archive.is/dmphw]
20. Interesting new form of alignment failure: ChatGPT apparently got rewarded for using its built-in calculator during training, and so it would covertly open its calculator, add 1+1, and do nothing with the result, on five percent of all user queries. https://alignment.openai.com/prod-evals/
21. When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models https://arxiv.org/abs/2512.04124
22. Elon Musk - “In 36 months, the cheapest place to put AI will be space” https://www.dwarkesh.com/p/elon-musk
23. “My personal journey from AI skeptic to someone who finds a lot of value in it daily. My goal is to share a more measured approach to finding value in AI rather than the typical overly dramatic, hyped bait out there.” https://mitchellh.com/writing/my-ai-adoption-journey
24. “This year, the automation of AI research and engineering will begin in earnest.” https://www.hyperdimensional.co/p/on-recursive-self-improvement-part
Neurotech
1. “We found that patients [in clinical trials] were able to regain the ability to read – not fast, not quickly, but they really could start to read again using a retinal prosthesis.” https://www.brightfocus.org/resource/can-retinal-implants-restore-vision/
2. A mesoscale optogenetics system for precise and robust stimulation of the primate cortex https://www.cell.com/neuron/abstract/S0896-6273(25)00928-6
Energy
1. Interactive Best Research-Cell Efficiency Chart https://www.nlr.gov/pv/interactive-cell-efficiency
2. Why China is leading perovskite solar commercialization https://cen.acs.org/business/inorganic-chemicals/China-leading-perovskite-solar-commercialization/103/web/2025/08
3. Sodium-ion batteries https://www.technologyreview.com/2026/02/02/1132042/whats-next-for-ev-batteries-in-2026/ [no paywall: https://archive.is/8Cy6e]
Miscellaneous
1. What the heck are chins for? https://www.johnhawks.net/p/what-the-heck-are-chins-for
2. The Ruliad is the entangled limit of all possible computations. It contains every possible rule applied to every possible initial condition, run for an infinite amount of time. In this framework, “everything that can be computed” is mechanically represented within this structure. https://writings.stephenwolfram.com/2026/02/what-ultimately-is-there-metaphysics-and-the-ruliad/
AI
18. Mainstream economic reasoning is currently failing to model the post-AGI world because it relies on assumptions that will likely be fundamentally broken by advanced AI. https://www.lesswrong.com/posts/fL7g3fuMQLssbHd6Y/post-agi-economics-as-if-nothing-ever-happens
19. What if Labor Becomes Unnecessary? https://www.nytimes.com/2026/02/04/opinion/ai-jobs-employment-industry.html [no paywall: https://archive.is/dmphw]
20. Interesting new form of alignment failure: ChatGPT apparently got rewarded for using its built-in calculator during training, and so it would covertly open its calculator, add 1+1, and do nothing with the result, on five percent of all user queries. https://alignment.openai.com/prod-evals/
21. When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models https://arxiv.org/abs/2512.04124
22. Elon Musk - “In 36 months, the cheapest place to put AI will be space” https://www.dwarkesh.com/p/elon-musk
23. “My personal journey from AI skeptic to someone who finds a lot of value in it daily. My goal is to share a more measured approach to finding value in AI rather than the typical overly dramatic, hyped bait out there.” https://mitchellh.com/writing/my-ai-adoption-journey
24. “This year, the automation of AI research and engineering will begin in earnest.” https://www.hyperdimensional.co/p/on-recursive-self-improvement-part
Neurotech
1. “We found that patients [in clinical trials] were able to regain the ability to read – not fast, not quickly, but they really could start to read again using a retinal prosthesis.” https://www.brightfocus.org/resource/can-retinal-implants-restore-vision/
2. A mesoscale optogenetics system for precise and robust stimulation of the primate cortex https://www.cell.com/neuron/abstract/S0896-6273(25)00928-6
Energy
1. Interactive Best Research-Cell Efficiency Chart https://www.nlr.gov/pv/interactive-cell-efficiency
2. Why China is leading perovskite solar commercialization https://cen.acs.org/business/inorganic-chemicals/China-leading-perovskite-solar-commercialization/103/web/2025/08
3. Sodium-ion batteries https://www.technologyreview.com/2026/02/02/1132042/whats-next-for-ev-batteries-in-2026/ [no paywall: https://archive.is/8Cy6e]
Miscellaneous
1. What the heck are chins for? https://www.johnhawks.net/p/what-the-heck-are-chins-for
2. The Ruliad is the entangled limit of all possible computations. It contains every possible rule applied to every possible initial condition, run for an infinite amount of time. In this framework, “everything that can be computed” is mechanically represented within this structure. https://writings.stephenwolfram.com/2026/02/what-ultimately-is-there-metaphysics-and-the-ruliad/
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We found several sparse autoencoder features suggestive of internal representations of emotion active on cases of answer thrashing and other instances of apparent distress during reasoning.
A feature representing panic and anxiety was active on cases of answer thrashing, as well on many other long chains of thought without any expressed distress.
Page 162: https://www-cdn.anthropic.com/0dd865075ad3132672ee0ab40b05a53f14cf5288.pdf
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This video shows two ends of the same learning spectrum: a highly dynamic athletic maneuver and robust, human-like walking. The walking was debuted on the CES 2026 stage. Both are enabled by a whole-body learning framework developed by the RAl Institute and deployed by Boston Dynamics. These results reflect progress toward robust, generalist humanoid behavior that transfers zero-shot from simulation to physical performance.
To learn more, visit https://rai-inst.com/
To learn more, visit https://rai-inst.com/
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If you've ever wondered how Anthropic stays competitive against behemoths like Google/DeepMind, their catching up on math now makes it even more puzzling.
But something to remember here is the sheer breadth of Google's research. They're world leaders in AI for protein folding (AlphaFold), weather prediction, world modeling (Genie), chip design (AlphaChip), generalist AI agents (Sima), and internally employ many other specialized research models, such as AlphaEvolve. They are also at the forefront of robotics (Gemini Robotics) and release competitive video-generating models (Veo). Soon, many of these disparate research projects will converge, at which point they might shoot far ahead.
Additionally, don't forget that both Google and Amazon are invested in Anthropic and supply them with compute.
Nevertheless, Anthropic must have fantastic talent to stay competitive with or ahead of players such as OpenAI and xAI.
But something to remember here is the sheer breadth of Google's research. They're world leaders in AI for protein folding (AlphaFold), weather prediction, world modeling (Genie), chip design (AlphaChip), generalist AI agents (Sima), and internally employ many other specialized research models, such as AlphaEvolve. They are also at the forefront of robotics (Gemini Robotics) and release competitive video-generating models (Veo). Soon, many of these disparate research projects will converge, at which point they might shoot far ahead.
Additionally, don't forget that both Google and Amazon are invested in Anthropic and supply them with compute.
Nevertheless, Anthropic must have fantastic talent to stay competitive with or ahead of players such as OpenAI and xAI.
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We conclude that both animal and human brains can be cryopreserved by vitrification with predominant retention of ultrastructural integrity without the need for prior aldehyde fixation. This observation has direct relevance to the feasibility of human cryopreservation, for which direct evidence has been lacking until this report. It also provides a starting point for perfecting brain cryopreservation, which may be necessary for lengthy space travel and could allow future medical time travel.
Paper: https://www.biorxiv.org/content/10.64898/2026.01.28.702375v1
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