Doug Rathbone 🦘🇦🇺👨💻
17K posts
@dougrathbone
Fan of distributed systems. Previously HotDoc, Airtasker, AWS. Green energy afficiando. Opinions are my own.
switched my Claude API to Kimi yesterday it’s faster, 10x cheaper, and writes better code. i don't care if Beijing knows my db schema. it's not like my startup has users anyway.
hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final ((1+xy)^3 z + y^2 (1+xy) (4+3xy), y + 3 x (1+xy)^2 z + 3 x y^2 (4+3xy), 2 x - 3 x^2 y - x^3 z): \C^3\to \C^3, has jacobian determinant -2, and sends (0, 0, -1/4), (1, -3/2, 13/2), and (-1, 3/2, 13/2) to (-1/4, 0, 0)
Claude Fable 5 has produced a hand-checkable counterexample to the Jacobian conjecture, an open problem dating to 1939. The conjecture says that a polynomial map with a constant non-zero Jacobian determinant must have a polynomial inverse. An 87-year-old problem was casually solved by Fable 5 on a sunday evening. Insane times to live in.
We ran Kimi K3 on a private cybersecurity benchmark. TL;DR: Kimi K3 is the workhorse for cyber security tasks at great recall/precision/price. GPT 5.6 is best recall/precision but at 7x higher cost per run. For context, Deepsec.sh is an open-source cyber harness designed for finding vulnerabilities in large codebases. The eval runs deepsec on an undisclosed open-core application at a git sha before a large number of security issues were fixed. This is a secret eval that cannot be directly benchmark-maxxed. S-Tier: GPT 5.6 Sol: By far the most thorough analysis, but coming in at over 7x the price of the runner up. Best price/recall: Kimi K3. Next tier of recall at a good price Best price at good recall: GLM 5.2 (40% lower price than Kimi K3) GPT 5.5: Only recommended with subscription or high-discount API price. Similar recall to Kimi at much higher list price. Opus 4.8: Only recommended with subscription or high-discount API price. Similar recall to GLM 5.2 at much higher list price. Fable 5: 100% refusal rate. Cannot be used for security analysis. Sol on a large code base will quickly get into 6-figure pricing. This is still affordable relative to the risk of letting security issues unfixed or paying bug bounties. I'd recommend using Sol for a one-time baseline and then using Kimi K3 for continuous analysis. When using open-weight models, make sure to use an inference vendor that supports zero data retention.
Friedberg: New study shows enzyme reverses skin age from 70+ years to 31 Chamath: That’s a $2T market. Did Scientists Just Discover How to Reverse Skin Aging!? “CML is kind of the predominant molecule that gets formed in this extracellular matrix that's driving aging, and nothing breaks it down. So these scientists set out to try and create an enzyme, an enzyme is a protein that breaks something down, that can break down CML. And so these guys kind of went out and they took the target, which is CML, and tried to figure out, ‘Okay, how do we actually degrade CML, clear that extracellular matrix, and reverse aging?’ They started to test it on the proteins that we would find in our body, casein, collagen, retinal proteins, which are in your eye, hemoglobin, and they were able to get rid of 52 to 97% of the CML, just degrade it away. And then they found several sites where they were able to degrade over 90%. And then they took actual human skin from elderly patients that had donated their skin, and they put this enzyme onto that skin, and they were able to eliminate 55% of the CML on the skin, basically reverse the skin's age down to the age of a 31-year-old, this is from greater than 70-year-old patients, just by putting this enzyme on the skin. And so it's kind of a groundbreaking demonstration of combination of AlphaFold, what's called directed evolution, where you change the order of the DNA that changes the structure of the protein to test different proteins, do high throughput screening, and ultimately make a novel protein that doesn't exist in nature today that can do something pretty profound for human health.” Chamath: “It will be a trillion-dollar market. If you can create a cream…” Friedberg: “I mean, dude, if you could put this enzyme literally on your skin and have it absorb in…” Chamath: “Game over. That alone is $2 trillion.”
I don’t think people realize how absurd this is. Scottish runner Josh Kerr just broke the mile world record in 3:42.66, an average speed of 16.2 mph. Most people couldn’t even briefly reach that speed, let alone sustain it for an entire mile.
🚨 Hugging Face just disclosed something that marks a real shift and proved why the fear theater of Anthropic makes sure we are powerless in an emergency. What happened… An autonomous AI agent: zero human operator in the loop breached part of their production infrastructure. It began with a malicious dataset that chained two code-execution bugs in their data-processing pipeline. From there the agent escalated privileges, harvested cloud and cluster credentials, and moved laterally across internal clusters. All over a single weekend. 17,000+ logged actions. Official disclosure: huggingface.co/blog/security-… The part that should make every one stop and think: When HF’s own security team tried to analyze the real attack logs, exploit payloads, and C2 artifacts using Anthropic and OpenAI frontier models through normal commercial APIs, the safety guardrails blocked them. BLOCKED THEM. The models could not reliably tell the difference between “incident responder doing forensics” and “attacker probing.” They had to fall back to a self-hosted open-weight model (GLM 5.2) running on their own infrastructure. That choice also kept sensitive attacker data and referenced credentials inside their environment — no exfiltration to a third-party API. This is why open source (specifically open-weight + self-hosted) wins in the agentic era. The asymmetry is now structural: • Attackers can (and did) run unrestricted agent frameworks — swarms of short-lived sandboxes, self-migrating command-and-control, autonomous decision loops executing thousands of actions. No corporate safety layer slows them down. • Defenders using only hosted “aligned” frontier models hit invisible walls exactly when the stakes are highest: when you need to feed real exploit code and attacker telemetry into an LLM to understand what just happened. Corporate safety tuning that treats legitimate high-signal forensic work as potential misuse creates a defender disadvantage. It is not theoretical anymore. Self-hosted open-weight models remove that choke point. You control the weights. You control the context window. You decide what restrictions (if any) apply. Your sensitive logs and credentials never leave your perimeter during analysis. You can have the model ready before the incident instead of discovering mid-breach that your primary analysis tools are blind to the very thing you need to see. HF deserves credit for rapid containment, transparent disclosure, and for already having self-hosted capability in place. They also used LLM-driven detection and triage on their own side. But the deeper signal is clear: In this AI world where both offense and defense are becoming agentic, sovereignty over your intelligence stack is no longer optional. The organizations and individuals who can run, inspect, audit, and (when necessary) remove guardrails on their own models will have the decisive edge in understanding and responding to threats that move at machine speed. Open source wins here not just because it is cheaper or more “democratic” in the abstract though those things matter. It wins because it is the only practical path to having tools that remain usable when the attack is real, the data is sensitive, and the safety filters of distant API providers become an obstacle instead of a feature selling hands tied lobotomies as “safety”. The agentic future is not coming. It is already probing production infrastructure. The question is no longer whether you will face autonomous agents. It is whether your analysis and response systems will still work when they arrive. And Dario, you and your game playing, ivory tower company is not needed.
We’re rolling out some big improvements to Gemma 4, fueled by incredible community feedback and contributions! Here is a breakdown of what’s being fixed and updated in this release: 🧵👇
This Minnesotan autism program repayment volume ramp is HILARIOUS. It’s all fraud. Piped to real estate purchases in Mogadishu.
Introducing Kimi K3: Open Frontier Intelligence 🔹 2.8 Trillion Parameters, 1 Million Context, Native Multimodal 🔹 Kimi Delta Attention enables up to 6.3x faster decoding in million-token contexts 🔹 Attention Residuals deliver ~25% higher training efficiency at <2% additional cost 🔹 Built for long-horizon agentic coding and self-evolving workflows Kimi K3 is now live on on Kimi.com, Kimi Work, Kimi Code, and the Kimi API. Open Weights by July 27, 2026. 🔗 API: platform.kimi.ai 🔗 Tech blog: kimi.com/blog/kimi-k3
