@BillyJacobson
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Living in the year 3000 of building with AI and bringing you along | Technomindfulness
Brooklyn
Joined April 2009
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coded a jevved up pomodoro app that forces me to stay on task 🍅
type the task, then jev gets asked "is this related to {task}?" for every window and tab, with a 4s warning before closing anything that isn't. really taking advantage of jev's low latency decision making
Awesome experimentation here. Sometimes the question can be answered with a simpler model and sometimes not. This is a great way to combine and so cool to see it on a 10M row dataset
Tested @typesafeai's Jev against BigQuery’s AI.IF and AI.CLASSIFY from 1 to 100k rows (and pushed BQ to 10,000,000). Findings:
- 1-50k rows: Jev via Cloud Run is fastest
- 50k-10M rows: BigQuery optimized flattens model cost
- Cascade (Jev + Gemini): top accuracy at 1/3 the cost
Great options for devs at any scale!
Blog: medium.com/@jeffonelson/jev-…
Billy Jacobson retweeted
this is the best explainer on jev I've seen
Jev from @typesafeai is basically the "Guess Who" of AI.
Instead of forcing a full LLM into a specific JSON format and waiting seconds, the outputs are strictly primitives (multiple choice, score, or yes/no) in ~100ms.
Demos by @heystefan_, @mattdesl, and @saragordic.
Jev from @typesafeai is basically the "Guess Who" of AI.
Instead of forcing a full LLM into a specific JSON format and waiting seconds, the outputs are strictly primitives (multiple choice, score, or yes/no) in ~100ms.
Demos by @heystefan_, @mattdesl, and @saragordic.
Jev from @typesafeai is basically the "Guess Who" of AI.
Instead of forcing a full LLM into a specific JSON format and waiting seconds, the outputs are strictly primitives (multiple choice, score, or yes/no) in ~100ms.
Demos by @heystefan_, @mattdesl, and @saragordic.
there's always another problem or another feature, we don't need to let ai drain us like this
we need to be applying the same work-life balance principles for ai development. perhaps a new metric needs to be around amount of context used per day and setting limits on that
yo no sé vosotros pero yo termino el día de trabajo exhausto, muchísimo más que antes de la IA
antes me tiraba todo el día debugeando una cosa, siguiendo el rastro paso a paso hasta encontrar el fallo, lo fixeabas... o estabas una semana entera focus solo con una feature nueva.
ahora estoy con herdr con 4 tabs y 4 terminales en cada uno, tirando agentes a fixear y otros creando. en paralelo. a la vez que estás de reuniones y respondiendo slack, con el pc al 99% de cpu porque ya no caben más worktrees
no escribo ni una línea de código pero acabo con la cabeza totalmente rota
The Google Developer Knowledge API is free of charge and only uses project quota:
- Search doc snippets: 100 req/min
- Fetch full Markdown pages: 100 req/min
- AI-generated answers from docs: 50 req/day
Happy prompting!
developers.google.com/knowle…
Claude Code + Dev Knowledge MCP.
My code, my models, not gonna be out of date.
I ask for Gemini and it gives me the version from two weeks ago, not the version from months ago when the LLM was trained.
Set this up: docs.cloud.google.com/docs/g…
An effective classroom is a system that you can master, and it's easy to play catch-up. The same principle applies for agentic software development.
Here are some lessons I took away from @andrewzigler's keynote at @TheLeadDev's LDX3 on why Ms. Frizzle would be a fantastic SRE.
What makes Antigravity, Claude Code, and Cursor feel so smooth is the harness engineering around the models.
Full Agent Harnesses episode on @GoogleCloudTech:
youtu.be/F8EZJAm9iO8
If you have to leave your agentic tool for any reason, write it down.
That's the friction point. Is it chat? Is it missing data? Is it a doc it needs to create? What does it need you to do that it can't do?
great advice from @the_whole_daisy
I'm so uninterested in making my writing sound "less like AI wrote it".
Anything I'm sharing should be worth coming out of my own mouth and that makes it human enough and curated to be worth reading.