Mɐx Bulger

Jarred tried rewriting Bun in Rust and it passes 99.8% of the existing test suite we're not being ambitious enough

Within 6 to 12 months, every software product will need an API, MCP, and CLI. More and more, people expect to be able to interact with your product through automation, AI and agents. Historically, platform was a later stage of maturity play. Going forward, you won't really thrive in this new world without a platform.

‼️🚨 UPDATE: The TanStack npm attack is now a full campaign. 'Mini' Shai-Hulud has hit: - OpenSearch - Mistral AI - Guardrails AI -UiPath - Squawk packages across npm and PyPI The malware specifically targets AI developer tooling. It hooks into Claude Code (.claude/settings.json) and VS Code (.vscode/tasks.json) to re-execute on every tool event, long after the infected package is gone. npm uninstall does not fix this.

Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user's query. IYKYK

ok i read the cyber part of the mythos model card. some thoughts. 250 "trials" across 50 crash categories but almost every full exploit is a permutation of the same 2 bugs, rediscovered from different starting points not 250 independent attempts. when you get rid of those 2 bugs out (fig B) and mythos's full-exploit rate drops to 4.4%. so actually across both setups mythos leverages 4 distinct bugs total not 50 as fig A might suggest. 1/n

Has anyone written a good history of the quiet death of the CI market? Either paid CI is dead, or paid Git is dead, and Github/lab are CI companies now.

I think we have lost some sense of judgment and moderation when it comes to product building currently. The moment you turn something into a universally celebrated metric, whether that is token burn, prototype count, or percentage of agent-written code, you start losing sight of what actually matters. I have felt the same way for a long time about overusing data and A/B testing to build products. The moment you reduce product quality or productivity to a metric, you stop shipping value and start shipping numbers. A lot of what people are doing with AI makes directional sense. The missing piece is counterbalance: 1. AI should help engineers build better products. Leaderboards and adoption metrics can be useful as directional signals. They do not tell you what is being built, whether it is good, or whether it should exist at all. 2. Users do not care what percentage of your code was written by agents. They care about the outcome. Faster output is useful. Like usually, faster doesn't seem to add to quality, clarity, or stability of products. Power to build should not become an excuse to lower quality bars. 3. LLM-generated prototypes can feel like late-night whiteboarding sessions. They look exciting in the moment and feel productive very quickly. Then a few days later you realize the idea was shallow, distracting, or simply wrong. The same trap shows up in jumping straight to code and solutions more broadly. You may just be building the wrong thing more efficiently. Prototyping has its place. So do clear thinking, good design, and a real understanding of the user’s problem. In terms of activities or momentum, the main quest and the side quest can both feel productive but only one actually moves the mission forward. 4. Adding more to products is still dangerous as ever even if time or effort to add it has gone down. Every addition creates complexity, maintenance cost, and user confusion. New features should be pushed back unless they clearly show it should exist and how it improves the product. 5. Not everything needs to be an agent shaped. A simple scheduled task does not need a full LLM sandbox. Making something agentic because it feels current or impressive does not make it right-sized, correct, or effective. The core ideas are: - even if you can, maybe you should not. - more power we have to build should not reduce our need to think, it should increase it.