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jev is INSANE for email. sorted 1,000 gmail messages in 76 seconds. cateogies: needs reply, updates, promos, sales, spam. parsed every single one. total cost $0.03. might just make this my daily mail client now. open source, link below TypeSafe AI

74,695 次观看 • 10 天前 •via X (Twitter)

8 条评论

Fazle Rahman 的头像
Fazle Rahman10 天前

repo:

Ahmet Saridag 的头像
Ahmet Saridag9 天前

@typesafeai original 🔥👀

Cartwright 的头像
Cartwright9 天前

@typesafeai You Can sort up to 232 mails sek so 1000 would Only Take 4-5 sek with jev

Johnny Nel | AI for Founders 的头像
Johnny Nel | AI for Founders9 天前

@typesafeai Email does that already, what am I missing?

Asaf Mazuz 的头像
Asaf Mazuz9 天前

@typesafeai Already in my directory 😄 Thank you so much for sharing this! 🙏

Chen 的头像
Chen9 天前

@typesafeai brutally accurate. lived a version of this last quarter

Pranav 的头像
Pranav9 天前

@typesafeai what if Outlook and Gmail implement this for all users 🤷🏻‍♂️

Ryan Huellen 的头像
Ryan Huellen9 天前

@typesafeai Waiting for this animation would drive me nuts. Needs to parallelized - assuming this is for the tweet.

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If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

Ruben Hassid

171,725 次观看 • 8 天前

i genuinely don't understand why everyone isn't using this yet diogo almeida, one of the people behind chatgpt, spent two years in stealth and shipped this on monday. his own words about it: "jev is off the charts", the same frontier intelligence you're already paying for, "two orders of magnitude faster and more efficient" in normal words: jev is an ai that never writes anything. it only decides. yes or no, which pile does this go in, how good is it. one answer per item, under half a second each, with a number for how sure it is. it isn't writing anything, so it can't make anything up so you stop feeding chatgpt one thing at a time and hand jev the whole pile. 400 unread emails into reply today, later, never. 300 comments into questions, complaints, praise. 200 job posts into worth it and not. a competitor's entire ad library into what's actually working. the whole pile, in seconds, for cents full guide: > join the waitlist on people are approved the same day > open the dashboard and create an api key > tell claude or your agent: install the typesafe skill > paste your pile and name the buckets you want it sorted into > it hands everything back labelled, with a confidence number on each one no code, nothing to learn. someone ran 1,891 ads through it in 19 seconds for 12 cents, and it's free on vercel's gateway right now five minutes to set up, and the sorting job that used to eat your evening is done before you stand up full step-by-step guide, link below bookmark this

Argona

32,058 次观看 • 10 天前

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Superior Agents

12,203 次观看 • 1 年前

.Dwarkesh Patel: By 2030, it will be less expensive to monitor every single nook and cranny in America than it is to remodel the White House. “Mass surveillance is, at least in certain forms, already legal. It has just been impractical to enforce so far. Under current law, you have no Fourth Amendment protection against any data you share with a third party. That includes your bank, your ISP, your phone carrier, and your email provider. The government reserves the right to purchase and read this data in bulk without a warrant. What’s been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every single camera, read every single message, and cross-reference every single transaction. However, that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, then for 30 billion dollars, you can process every single camera in America. And remember that a given level of AI ability gets 10x cheaper every single year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it’ll be less expensive to monitor every single nook and cranny in this country than it is to remodel the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian state is the political expectation that this is not something we do here.”

Arjun Khemani

145,893 次观看 • 6 个月前

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Roan

169,974 次观看 • 9 天前

this is pure f*cking treasure Engineer at TypeSafeAI just mapped 389 public Jev builds in one list: skills, MCP servers, SDKs, agents, benchmarks and guides ROUTING > jev-router sends every Claude Code task to the cheapest model that can do it > Switchboard picks the model and reasoning effort per task, then keeps it stable so the prompt cache survives > jev-oncall triages alerts: 418 ms p50, $0.04 per 1,000 alerts GUARDRAILS > jev-axi gates shell commands for Claude Code and Codex, 44/44 on its labeled set > hermes-jev-approvals: 8.7x faster approvals, 4.4x fewer prompts to the user > Sniff Test lints AI slop out of your writing at 182 ms a paragraph AGENTS > Jev Ultrafast: Jev picks every click, a small LLM only types > fast-jev-compaction scores your context instead of summarizing it > jev-browser-use reports 5 to 10x faster browser runs inside Codex SEARCH & RAG > jev-retrieval placed 2nd of 90 models on a reranking leaderboard > jevsearch: 83% Hit@1 against 41% for keyword search alone SDKs & MCP > clients for Swift, Go, Rust, Kotlin, Ruby, Elixir, .NET, Laravel and Spring > MCP servers for Claude Code, Cursor and Codex > Jev inside SQLite, DuckDB and Postgres, straight from SQL EVALS > pytest-jev checks LLM replies in 5.3 s where Claude took 27.1 s > a medical hallucination check at 92.9% accuracy, 204 ms, $0.03 per 1,000 OPEN MODELS > Laya answers in one ~35 ms forward pass > kev trains and runs on a MacBook GAMES, ROBOTS, TRADING > Jev plays Mario, Pokemon and chess > it drives a robot arm and a drone > it trades on Monad with 81 ms decisions 389 builds. one decision layer. go steal the ones you need

NO1ennn

24,281 次观看 • 1 天前