Follow AI Builders — Not Influencers

Voice is the natural form factor to use computers.

If you have strong intuitions about the future, have high agency, and are creative... you need to put yourself in a position where you can act on those intuitions. Ideally with enough freedom to navigate and follow those intuitions as they change. 1 year in the singularity is worth 10 normal years. Think about the opportunity cost of spending a year in something you don't deeply believe in.

Apple's new macbook pro wallpaper reminds me of something 🤔

RT Brady Murphy We have the belief that voice is a more accessible and natural I/O mechanism for our users. Our voice system sits above all your sessions and projects so you can quickly move between decisions and artifacts.

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RT Sidharth Raja one thing i can absolutely say for certain after signing up to try, is that this was the fastest "time to api key" experience i've had for anything i've ever used

Vercel CI solves the "AI coding bottleneck" via: ① Fast and stable infrastructure (faster work) ② Turborepo integration (less work) ③ Agentic CI ① is obvious. Your CI is slow, flaky, goes down. We solve that. We operate 20 worldwide clusters doing millions of microVMs per day. ② is less obvious, but very powerful. Turborepo was designed to intelligently skip and cache prior work. The graph of dependencies is computed, not written. Ask your agent to migrate to @turborepo and start saving money and time. Even if you don't use us. But you should, it's really good. ③ is a game-changer. 𝚟𝚎𝚛𝚌𝚎𝚕 𝚌𝚒 in the CLI is the first step, because now your agents can inspect, abort, trigger, replay CI. Vercel Agent can help optimize it. And this is just the start. You can expect a lot of hooks and capabilities to make CI something far more intelligent, that can even capture the nuance and judgment an engineer would have ("Are those tests even worth running?") Agentic CI is the future of how we ship software. Excited to build it with you all!

RT lily clifford introducing prism, the world’s first speech-to-speech model you can’t tell from a human in conversation prism has: > 300ms response time, 4x faster than OpenAI GPT-Live > outperforms every other s2s model on intelligence (with NO reasoning) > the first s2s model customizable through reinforcement learning we’re a team of 35 with $35M in funding, and we’re unlocking s2s model adoption faster than openai, google, and x. the first 100 people to sign up here get 1 month free: https://rime.ai/prism?utm_campaign=prism-launch&utm_source=X&utm_medium=organic

we'll see when we test it. These charts dont matter

Introducing the Document Parsing Protocol (DPP) There are hundreds of different OCR models and tools, but they might all parse a document in different ways. Some errors are very clear-cut (e.g. hallucinating text, or completely misaligning a table), but many errors are much more ambiguous (e.g. how to represent split tables sharing a header). It's an issue that plagues most parse benchmarks too, because they inherently reward a certain style even if other styles might be semantically correct. We introduce a protocol that proposes a standard way of parsing these documents into a correct format. We've curated a list of 140 rules that cover literally every aspect of doc parsing, for instance: - tables: borderless whitespace tables, header-only tables, horizontal slicing, side-by-side mini tables - forms: key-value regions, checkboxes, checklists, ids and labels, fields outside the form, - charts: standardized chart output, multiple charts, gantt charts, line charts You can use DPP as an evaluation rubric for LLMs when determining the quality of parsing a document. It's not a full evaluation benchmark (yet), but an initial evaluation guide, and meant to encourage a discussion on what are the "best" ways to parse a document. Check it out: https://www.parseprotocol.com/guide/forms-forms-and-key-value-regions Blog: https://www.llamaindex.ai/blog/introducing-the-document-parsing-protocol