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Introducing NDI 1.0 - a small model for document processing. #1 on Parse Index Normalized (across 6 public benchmarks) GPT-6 Astra performance at 1/10th the cost 📈 Trained on 15M+ financial files for industry-grade accuracy. Get started with $25 in free credits: Reply "NDI" for a single file setup... show more
1,304,753 просмотров • 9 дней назад •via X (Twitter)
Комментарии: 43
OmniDocBench
ParseBench
MDPBench
ExtractBench
curious how it handles multi-page contracts with tables that span pages. most of these tools still choke on that.
Developer Access:
Developer Console Access
Already running drex in night desk, NDI for filings is the obvious add
1/10th the cost for financial doc parsing is massive.. specialized small models keep winning
how accurate is NDI on messy real invoices?
Making it cost efficient with such great results is a good one guys
The "every value links back to its source" part is the real flex. An agent you can audit beats an agent you have to trust.
bangerrr!!
love that you shipped a single file setup for Claude Code and Codex. how long does it take to get parsing from zero?
Pretty much right away, add your API key, and you can start parsing in a few minutes.
Ndi
check your DMs!
NDI
Check your DMs!
"astra performance at 1/10th the price" is the only benchmark founders actually read
trying this
Parse, split, classify, extract, ground. One document goes through all five and stays linked to its source.
I'll try it, it will help me a lot in my work
curious how much the 15M+ financial files bias it toward finance. does accuracy hold up on contracts or medical docs?
NDI
Your DMs are closed, DM us and we’ll send it over!
GPT-6 level accuracy at tenth cost on financial docs impressive
@pouyanroohii
Ndi
Your DMs are closed, DM us and we’ll send it over!
Hitting number one on the Parse Index with that kind of cost efficiency is seriously impressive
Affordable, high-accuracy document processing could be a game changer for AI workflows
Everyone's building smarter agents, but half of them are still squinting at blurry PDFs. Fixing the eyes first makes a lot of sense.
the mountain in that image is doing more marketing than the entire product page
Outstanding Nace AI launch, NDI 1.0 delivers top accuracy at a fraction of cost efficiently!
Cost per page? @grok
NDI
Check your DMs 🙌
interested
1/10th the cost.. you're cracked 🫡
NDI
Check your DMs!
15M files is a lot of data to train on
![Official OpenAI tip for Codex: stop burning GPT-6.1 Sol context on tasks GPT-6 Luna can swarm, while GPT-6 Astra sits idle put Astra on call as a dedicated adversary codex --model gpt-6.1-sol --agent adversary GPT-6.1 Sol acts as the lead architect, scoping architecture and validating pull requests GPT-6 Luna teammates take isolated git worktrees and hammer out implementations at medium effort GPT-6 Astra monitors the peer-to-peer bus as a dedicated adversary, only stepping in at three critical moments: → before a contract locks: does the frontend payload match the backend schema? → when a test breaks twice: are we patching the bug or just hiding the symptom? → before calling done: what attack surface or edge case did everyone overlook? GPT-6 Luna builds. GPT-6 Astra challenges. GPT-6.1 Sol merges and ships Jev engineering runs the identical pattern one layer down: mechanical decisions that need no reasoning (which file to open, which tool to invoke, retry or abort) execute in 16ms, so the frontier models only wake up when execution paths actually diverge Plan on high. Delegate on medium. Keep the adversary on peer-to-peer call. - the full team setup > GPT-6.1 Sol on high coordinates the session and synthesizes PRs > teammate-ux builds client state inside a separate git worktree > teammate-back writes endpoints and handles Redis atomic locks > teammate-adv runs invariant fuzzing and catches replay vulnerabilities > all teammates communicate directly over peer-to-peer messaging without coordinator drag > tasks.json manages file-level mutex locks so nobody steps on another worktree paste the setup and this prompt into Codex CLI below: "Configure my Codex workspace for autonomous agent teams: 1. Audit ~/.codex/agents and .codex/agents for teammate profiles fitting ux, backend, and adversary > Generate definitions only for missing roles > Pin model: gpt-6-luna, model_reasoning_effort: medium for workers, and model: gpt-6-astra for adversary > Assign each worker an isolated git worktree branch to eliminate file conflicts 2. Enable agent teams in ~/.codex/config.toml: > Set [agents] max_concurrent_threads_per_session = 8 > Lock main session model to gpt-6.1-sol with model_reasoning_effort = 'high' > Set approvals_reviewer = 'auto_review' 3. Initialize tasks.json in repo root with atomic file locking, allowing teammates to claim subtasks and resolve contracts directly over inter-agent messaging without coordinator intervention 4. Add a team rule to AGENTS.md: teammates must request an adversary security review before opening a pull request for the lead session Show every configuration diff first. Do not apply edits until confirmed" ↳](https://image.24vids.com/tw-2106127135278612573/amplify_video_thumb/2106126443818160128/img/um3FcaWLOtHQKOfu.jpg)
