Chris Forrester

@cerebras Introducing a sneak peak of Hypernym's Magic; Magic is a high-speed, high-fidelity codebase intelligence layer: Cerebras + GLM 4.7 for reasoning, Hypernym compression + pre-trace reasoning for efficiency, and Cline integration for direct agentic execution.

@theThinkInst @DivinelyDesined gradients. look up how Protozoa nom and tumble. en.wikipedia.org/wiki/Protozoa

@DivinelyDesined These little guys aren't alive. How are they moving like this? I get that "this is chemistry," but what is animating them?

Incredible. This is the DNA Repair System. Look how many parts work together to make the system function. Without DNA Repair, mutations in DNA would completely destroy its functionality very quickly - it would degrade rapidly into non-functional junk. Which means, the DNA Repair System would have to have been around since the very beginning of Life; DNA and the Repair Systems would have to arise together, at the same time, or DNA could not survive - and neither could Life. But how complex is the DNA Repair System? It requires 6-7 major systems, working in coordination together. Combined, those systems contain a total of about 130-200+ total unique proteins that make up the systems to do the job. Two things make this system most likely designed: 1. DNA cannot survive without DNA Repair. The entire system must be in place at the very start of Life, or Life never starts. But creating the systems requires the information in DNA - they both must arise together, simultaneously. 2. The specified, irreducible complexity of the system. DNA Repair requires a minimal amount of specifically engineered protein systems to function. Just look at how many separate systems are involved in that process! How can anyone see something like this and believe it arose by an accident of natural processes? Life is so clearly intelligently Created.

@HowToAI_ We noticed that as well. it's because the similarity Manifold converges as you increase the density. you really want small selectors. If done correctly, what happens is a "feature feather" that responds to the bit density of the selection field. hippocamp.hypernym.ai chat?

RAG is broken and nobody's talking about it. Stanford researchers exposed the fatal flaw killing every "AI that reads your docs" product in existence. It’s called "Semantic Collapse," and it happens the second your knowledge base hits critical mass. If you've noticed your AI getting "dumber" as you add more data, this is exactly why. Right now, companies are dumping thousands of documents into their AI, thinking it’s getting smarter. When you add a document to RAG, it converts it into a high-dimensional vector. Under 10,000 documents, this works perfectly. Similar concepts cluster together. But past 10,000 documents, the space fills up. The clusters overlap. The distances compress. Everything starts to look "relevant." It is a mathematical law called the Curse of Dimensionality. In a 1000-dimensional space, 99.9% of your data lives on the outer edge. All points become equidistant from each other. That perfect, relevant document you are looking for now has the exact same mathematical similarity as 50 completely irrelevant ones. The Stanford findings are brutal: At 50,000 documents, precision drops by 87%. Semantic search actually becomes worse than old-school keyword search. Adding more context doesn’t fix the AI. It makes the hallucinations worse. Your "nearest neighbor" search isn't finding the best answer anymore. It's finding everyone. We thought RAG solved hallucinations. It didn't. It just hid them behind math.

@mathemetica BEES

Diffusion (stochastic SDE sampler): erratic Brownian trajectories zigzagging through noise. Flow Matching (deterministic ODE integrator): clean, straight-line paths to the data modes. Same start, radically different dynamics.

doing some work on Edge models with our new inference stack and the @PrismML bonsai releases. gpt4 quality 8b 1-bit model, 1.3tps on 5 year old iPhone 13 pro, lightning fast loads + prefill with our stack in addition. the real forefront though? Apple **Watch Local**

@kareem_carr It's like that simpsons with homer and the bobbing head bird. It's amazing.

I keep hearing that software engineers don’t write much code anymore and it’s mostly AI now. Can any software engineers confirm how true this is? Do you just drink coffee and watch Claude code all day now?

@PrismML guys.. this is amazing. actually literally ONLY on the apple watch. zero network connectivity. *entirely* on-device. bravo.

Today, we are emerging from stealth and launching PrismML, an AI lab with Caltech origins that is centered on building the most concentrated form of intelligence. At PrismML, we believe that the next major leaps in AI will be driven by order-of-magnitude improvements in intelligence density, not just sheer parameter count. Our first proof point is the 1-bit Bonsai 8B, a 1-bit weight model that fits into 1.15 GBs of memory and delivers over 10x the intelligence density of its full-precision counterparts. It is 14x smaller, 8x faster, and 5x more energy efficient on edge hardware while remaining competitive with other models in its parameter-class. We are open-sourcing the model under Apache 2.0 license, along with Bonsai 4B and 1.7B models. When advanced models become small, fast, and efficient enough to run locally, the design space for AI changes immediately. We believe in a future of on-device agents, real-time robotics, offline intelligence and entirely new products that were previously impossible. We are excited to share our vision with you and keep working in the future to push the frontier of intelligence to the edge.

arstechnica.com/ai/2026/01/dev… next it'll be all developers enjoying managing full concurrent waterfall graphs; "productivity" is redefined - you are now limited only to the speed of the primary task. whole system thinking, critical planning. that is the entirety.

@NicerInPerson curious how quickly it exhausts your hourly limit?

I managed to unlock a crazy new hidden feature in Claude Code called Swarms. You're not talking to an AI coder anymore. You're talking to a team lead. The lead doesn't write code - it plans, delegates, and synthesizes. When you approve a plan, it enters a new "delegation mode" and spawns a team of specialists who: - Share a task board with dependencies - Work in parallel as teammates - Message each other to coordinate work Workers do the heavy lifting, coordinate amongst themselves, then report back.

@cerebras github.com/HypernymAI/Cli…

@cerebras Introducing a sneak peak of Hypernym's Magic; Magic is a high-speed, high-fidelity codebase intelligence layer: Cerebras + GLM 4.7 for reasoning, Hypernym compression + pre-trace reasoning for efficiency, and Cline integration for direct agentic execution.

@cerebras drive.google.com/file/d/1M7jbyU…

@withmartian Legit, we applied. hypernym.ai, runtime code compression coming soon.

$1,000,000 to understand how LLMs write code. Announcing: The Martian Interpretability Challenge. Understanding the inner workings of LLMs is the greatest scientific challenge of our age,. Let's solve it. Apply here: withmartian.com/prize 🧵👇

@hf0 the best way to stay ahead of the pack is to win the race.

@virlomain @xlr8harder don't talk about thermodynamics?

@xlr8harder WHAT'S THE FIRST LAW OF THERMODYNAMICS?? 😮‍💨😮‍💨🔥

Here's the Extropic paper. I gotta say, I'm no expert in this area, but it's starting to look awfully real to me. arxiv.org/abs/2510.23972

Hypernym's Continuous Curve Compression Chart the possible.

Blown away by the creativity & skill of the ~200 WeaveHacks hackers this weekend. Some of the most unique hackathon projects I've seen in years. Grateful to everyone who gave their weekend to come build self-improving agents with us 🔥 Check out what they built below!! Couldn't have done it without @altryne and @ReplayRyan. Also our judges spending their Sunday afternoons with us: @lavanyaai @adrnswanberg @dvdcrbt @iporollo @picocreator @realchillben @sgrove @VaguelySerious @svitlana_mm @Elationate

What scaling challenges have you hit lately? Always curious to hear how others are tackling inference under pressure.

Moments like this remind me why we build. It’s not just for performance, it’s for progress. 10/10 weekend. Nothing recharges me faster than watching brilliant people break things, fix them, and push what’s possible.

Sometimes chaos is the best classroom. You can’t simulate that kind of pressure. You only watch what survives it. 📸 Judging the @wandb Hackathon this weekend, hosted by @sammakesthings and @altryne from the W&B team.