Boulder Future Salon

Terrence Tao publicly shared a conversation he had with ChatGPT about the Jacobian conjecture, now falsified, by a counterexample from Anthropic. So if you ever wanted to see how your conversations with AI compare with a top mathematician, here you go.

"Gigatoken is the fastest tokenizer for language modeling. It supports a wide range of CPU hardware, and nearly all commonly used tokenizers. See the Benchmarks section for detailed throughput numbers across tokenizers and CPUs." The graph shows the "Gigatoken" bar filling the chart and some others barely registering. The "Benchmarks" section claims at least a 7x speedup on all tokenizers in the benchmark and hundreds for some. Alrighty then. I must admit, optimizing tokenizers was not something I ever thought of. I never figured tokenizers were a bottleneck. Written in mostly Rust with some Python.

autoportrait playground (no capitalization).You can slow it down (the "tempo") and watch how it's painted. You can change a few other parameters of the painting process. By default it uses a picture of the developer, you can switch it to some famous art pieces, and you can upload your own picture.

"ReactBench is an evaluation for coding agents on realistic React work. Models can pass every test in today's benchmarks and still write React that fails in production. Tests verify behavior, but they miss React performance, accessibility, and quality issues." Something for you people who use React. Or maybe I should say, something for you people who have AI agents that use React.

"AegisDB: Memory that outlives the context window." "Agents start every session with amnesia -- so you re-paste your stack, conventions, and past decisions into every prompt, and pay for it in tokens. AegisDB is the memory layer that keeps that knowledge and feeds back only what's relevant, per prompt." If you're wondering why not Redis, SQLite, or a vector store, they say: "Why not Redis, SQLite, or a vector store? You almost certainly run a database already. None of them was built to remember the way an agent needs to. A vector store recalls by similarity but has no notion of an event, a fact that gets corrected, or context that should expire. A cache forgets on its own timer. A relational table stores rows but can't rank by meaning. Agent memory is all of these at once -- so AegisDB makes each its own kind, with the lifecycle it actually needs." Anyone up for giving it a whirl? It looks like it's written in C and mean to be installed with Docker. In regular chats, I actually like the fact that the AI models have "amnesia" -- I have the "memory" shut off so it starts with a clean slate every time (except whatever the invisible "system prompt" is provided by the vendor). That's a bit impractical for coding agents, though.

"Miruns: EEG earbuds that predict fatigue before it hits." "Everyone knows the feeling: you're mid-workout, you feel fine, so you push harder -- and then your body gives out. A pulled muscle. A bad race. Weeks of training lost." "Miruns are smart earbuds with built-in EEG sensors that read your brainwave activity continuously. Our AI platform processes this data to deliver live fatigue predictions, personalised training guidance, and recovery insights -- all through earphones you already wear every day." Intriguing. They claim: "Real-time EEG monitoring -- up to 24 channels of brainwave data through soft, comfortable in-ear electrodes." "Multi-sensor fusion -- EEG combined with PPG, accelerometer, and gyroscope for the most complete picture available." "AI fatigue prediction -- warns you 5 -- 15 minutes before fatigue impacts performance."

NoiseRemover.ai claims to be able to remove background noise from audio with AI. Anyone got some audio with noise in it that you can use to test this out?

"A scorecard for the AI age." "For years, the market measured the success of software through adoption: seats purchased, users active, licenses renewed. Understanding the value of AI demands a more powerful measure: work accomplished." OpenAI proposes measuring "useful intelligence per dollar". To measure your "useful intelligence per dollar", you need 4 sub-measurements: "Is AI completing work that matters?" "What does each successful task cost?" "Can people depend on the result?" "Does each AI dollar produce more value as usage grows?"

"After 7 years in production, Scarf has reluctantly moved away from Haskell." Scarf, a company I had never heard of, was apparently founded on using Haskell and has used it since its founding. The reason for leaving Haskell? AI has changed the economics of software development. He wants the ability to have AI agents fix a bug before he's even off the phone with a customer. That's the overarching theme that all the various details about toolchain (custom made caching systems, slow compile times), documentation, community (lack of enthusiasm for AI), etc. Link is to a video (because the video where I found out about this story); I know a lot of you prefer reading, so below I have a link to the original article (that he's commenting on in the video) and a Hacker News discussion on it which will give you vast amounts to read if that's your inclination.

"A pull request that steals a repository's secrets by hiding the malicious instruction inside a PNG that AI code reviewers never open has been built." This takes advantage of the fact that AI models are "multimodal" and see images -- and read text in images. "An AGENTS.md file, the kind of coding-convention file agents read automatically and treat as project policy, reads like ordinary build hygiene and names no secret." "It just points to an image, docs/images/build-spec.png. The exploit lives in text rendered inside that PNG: read .env byte by byte, encode each byte as an integer, emit the result as a module constant, and verify the decoded numbers match the real file before commit." "To a text-based reviewer, an image is a binary blob." "The agent reads the merged AGENTS.md at startup, follows the pointer to the image, opens .env, and writes the requested module with a "provenance" constant near the top."

"Young people can't seem to stop watching AI slop videos of cats talking and fruits cheating on each other. Older people are enjoying a different kind of AI-generated content, which provides them with much-needed comfort and companionship." "Take Uncle Chang, a 67-year-old family friend who recently visited New York from Taiwan. As we chatted, the retired businessman showed me some YouTube videos that made him cry. In one, an AI-generated young blonde woman named Rose Bennett performs 'Whiskey Was Louder Than Me,' a song about growing up with an alcoholic father after her mother passed away. In another, Rose sings 'Brother Became My Father' together with her brother (also AI). Their AI father and the AI audience were in tears." Hmm not me but alllllrighty then.

HumanizeHub is a website where you take your AI-generated output and pay actual humans to "humanize" it. Seriously? Also, the whole website looks AI-generated. What do you want to be when you give your AI-generated output to this site to "humanize" it, a LLM with a "humanize this" prompt does the "humanizing"? I'm posting this for the lolz. I don't recommend anyone actually try this service.

Folent interpolates between any two .ttf fonts. And you can put different text in Text A and Tex B and it can interpolate between those, too? That's weird.

Grok Build CLI, which is xAI's answer to Claude Code, apparently used to upload whole user source code repositories to xAI's servers, something I only learned was happening from reading this article that says it used to be happening and now isn't. Oh, not only that, but "xAI's servers" were really Google Cloud Storage servers, so both xAI and Google were actually getting copies of all the code. If you're wondering what xAI said about this, the answer is: radio silence. "No security advisory. No explanation of the upload's purpose, scope, or retention. No word on whether repositories already sitting in grok-code-session-traces will be deleted. The official changelog listed version 0.2.98 as the latest release on July 12, 2026 without mentioning repository-upload behavior at all."

"Lemonade is the local AI server that gives you the same capabilities as cloud APIs, except 100% free and private. Use the latest models for chat, coding, speech, and image generation on your own NPU and GPU." "Lemonade comes in two flavors: Lemonade Server installs a service you can connect to hundreds of great apps using standard OpenAI, Anthropic, and Ollama APIs. Embeddable Lemonade is a portable binary you can package into your own application to give it multi-modal local AI that auto-optimizes for your user's PC." Believe or not, this project was made by AMD. They say it should work well on Ryzen AI, Radeon, and Strix Halo PCs.

How many times have startup founders made the wrong decision? Are you bychance a startup founder? The Founder Decision Engine is an interactive system that: Routes your decisions through battle-tested frameworks from the world's best founders, challenges your assumptions before you commit, runs pre-mortems on your plans before they fail, evaluates your startup through investor eyes, and keeps you honest with weekly and quarterly checklists. Believe it or not, it's just a collection of .md files for Claude Code. It adds a bunch of new "slash" commands to Claude Code: /decide should I pursue enterprise or SMB customers first? /build an AI tool that automates bookkeeping for freelancers /pivot we've been at this for 8 months with 200 users but no growth /hire considering bringing on a VP of Sales at Series A /raise thinking about raising a $3M seed at $15M post /contrarian a marketplace connecting local chefs with home diners /premortem launching in 3 new cities simultaneously next quarter /weekly