Abide is an open-source CLI and hook/plugin system that enforces project instructions for coding agents. It compiles AGENTS.md, CLAUDE.md, and other instruction files into a local rubric, then checks each edit and completed turn against the applicable rules. For each check, it sends the rule and changed code to Jev, TypeSafe's decision model, which returns a probability rather than free-form text; violations above the configured threshold produce a repair request for the agent. It supports Claude Code, Codex, and OpenCode, and also provides commands for auditing existing files, checking uncommitted changes, replaying sessions, reporting rule activity, calibrating rules against git history, and measuring latency and cost. The rubric is stored in .abide/rubric.json, while changed lines are sent to TypeSafe under the user's key with zero data retention requested. The project is licensed under MIT.
ABot-Recon is a streaming 3D-reconstruction model and inference tool for reconstructing long video streams from video frames alone. At each step, it caches key-value features from the preceding 11 frames, predicts a point map in the current camera coordinate system, estimates the adjacent relative camera pose, and composes those poses sequentially into a global trajectory and point cloud without persistent learned long-range memory. It can output camera poses, relative poses, local and world point maps, colors, confidence maps, and run metadata. An optional loop-closure backend retrieves candidate revisited-frame pairs using DINOv2-SALAD descriptors, predicts relative-pose constraints with ABot-Recon, and refines the trajectory through sparse pose-graph optimization. The repository provides Python and command-line interfaces, a public model checkpoint, a demo, evaluation code, and visualization utilities. The released configuration targets Linux, Python 3.10 or later, PyTorch 2.5.1, and CUDA 12.1; source code is licensed under Apache License 2.0, while the model weights have separate terms in MODEL_LICENSE.md.
Bashka is a command-line safety guard for the `curl ... | bash` installation pattern. It parses incoming shell scripts, scores them against checks for credential theft, data exfiltration, reverse shells, destructive commands, insecure downloads, persistence, obfuscation, and other hazards, and can follow forwarded or nested scripts so each layer is reviewed before execution. It can also provide the script for manual or AI-assisted analysis. Bashka records software installed through it in an installation registry, including detected binaries and created directories. Its commands can list and inspect recorded packages, rerun their installers, and remove tracked files. It supports configuration for review behavior, remote-script following, recursion depth, command limits, trusted domains, and interface style. The project is open source under the MIT license and is distributed as a shell-installed or prebuilt binary, including through Homebrew, Cargo, mise, and GitHub releases. Its documentation notes that static analysis cannot reliably detect every behavior in a Turing-complete shell script, that direct paths such as `/bin/bash` bypass its PATH shim, and that the registry cannot manage files whose installation locations it cannot detect.
Binance Futures is Binance’s futures-trading platform. In the cited context, recorded futures-market data from it is used for market-making backtests.
Compositor is a free, open-source image editor for macOS built around Photoshop-style compositing and post-processing workflows. It uses layers and folders with blend modes, opacity, masks, clipping masks, adjustment layers, non-destructive transforms, selections, painting and retouching tools, content-aware fill, filters, and multiple project tabs. It imports JPEG, PNG, HEIC, and TIFF files, and exports JPEG images; Photoshop-style keyboard shortcuts are supported throughout. The repository provides an Xcode project and documents macOS 26 and Xcode 26 requirements. It is licensed under the MIT License.
Expo is a React Native development and deployment platform for building native iOS, Android, and web apps from a shared codebase. Its CLI and development tools support local development, device and simulator testing, web support, app-store builds and submissions, and automated workflows for builds, tests, releases, and over-the-air updates. The platform also provides cloud simulators, production performance monitoring, hosting, and APIs for native app capabilities.
expo-gpt-live is an Expo SDK 57 voice-app template for iPhone that connects GPT-Live 1 with web search, a weather tool, selectable voices, and six AI Elements Persona variants. Users can speak during an AI reply, change the animated persona during a call, and keep a conversation running in the background on iOS. The project includes a local server that generates an app access token and relays requests using an OpenAI API key kept server-side. It uses native WebRTC and Rive components, so it requires a native build rather than Expo Go; the repository documents development on macOS with Xcode and deployment to a connected iPhone, as well as web and Android commands. Background calls are implemented on iOS only, and calls end after ten minutes. The repository is MIT-licensed. Its setup requires Node.js, macOS with Xcode, and an OpenAI API key with access to GPT-Live 1; the README advises adding authentication before exposing the backend publicly.
fast-jev-compaction is an npm library and Claude Code plugin that compacts tool-call history without rewriting the original text. It pairs each tool call with its result, sends the conversation state and questions about each non-pinned call to Jev, and then keeps the call and result, keeps only the call while truncating the result, or removes both according to Jev's scores. Recent messages and the first message are pinned; state fitting progressively truncates inputs, abridges long text, collapses older messages, and reduces old tool calls to one-line records so requests fit within configured token estimates. User and assistant text is never removed or shortened in the returned transcript, and retained content stays verbatim. The package exposes compaction, decision, batching, state-fitting, and transport-building functions, while the Claude Code hook replaces built-in compaction and falls back to Claude Code's summary when Jev fails or reduction is insufficient. It uses the TypeSafe API by default and requires a TypeSafe API key.
FLYON is a read-only EVM wallet tracker and command-line tool that extracts swap logs from a chain, reconstructs closed trades using first-in-first-out accounting, and ranks realised profit in each pool’s quote asset rather than in dollars. It sets aside proceeds from tokens whose purchases the chain cannot show, leaving those amounts off the leaderboard. When a watched wallet buys, FLYON records a call with its confidence before the outcome is known. Calls are stored in an append-only, hash-chained JSONL ledger and later settled using the first trade in the same pool at or after the configured window; calls without a qualifying trade remain open. It scores settled calls with the Brier score and builds a static site containing the trades, board, feed, calls, settlements, and scores. The tool uses six read-only JSON-RPC methods and has no API account, signing, wallet, or transaction-submission capability. It supports demo markets and replayable recorded indexer runs, requires Python 3.10 or later, has zero dependencies, and is released under the MIT license.
HFTENGINE is a replay console and backtesting application for market-making strategies using the hftbacktest crate. It runs tutorial strategies against recorded Binance USDT-M Futures order-book and trade data, replaying the local order book, resting orders and estimated queue positions, feed and order latency, market trades, executions, raw collector feed, and runner statistics frame by frame. Its Rust runner records the replay and its Vite/TypeScript dashboard presents the session in a text-mode interface. The project models post-only limit-order fills with hftbacktest's queue and exchange models; queue position is estimated from level-2 data, and order latency is derived from recorded feed latency rather than measured live. It includes tools to collect public Binance websocket data, convert recordings into sessions, run the backtest, and inspect replay moments. HFTENGINE is explicitly a backtester and replay screen: it does not place orders, compute or display P&L, or configure live trading.
Home Assistant is open-source home-automation software developed by a worldwide community of tinkerers and DIY enthusiasts. It provides local control and privacy-focused operation for connected devices and actions, with a modular integration system that allows support for additional devices and automations to be added. It can run on a Raspberry Pi or a local server and is available with installation instructions, tutorials, documentation, and a web demo.
Home Assistant ESP Screens is an MIT-licensed firmware and Home Assistant add-on for turning supported ESP32 touchscreen panels into configurable control screens. It supports the 2.8-inch CYD and 4-inch Guition panels, with tiles for lights, climate, blinds and curtains, vacuums, media, weather, history graphs, cameras, clocks, and timers. The ESP Screen Manager app runs inside Home Assistant. Users select entities and actions, arrange tiles by dragging them across pages, and save the layout to the screen without writing YAML, using MQTT, or manually reflashing for ordinary changes. The app builds and installs firmware over USB, sends updates over Wi-Fi, and can update screens through OTA; the firmware uses LVGL and communicates with Home Assistant through ESPHome. Screens can also display alerts, camera images on supported Guition hardware, dark mode, brightness, standby, night-hour settings, and automation-controlled actions.
Jev Review is a local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev. It runs as a local Node.js server over MCP stdio and exposes a focused `jev_review` tool: an agent submits a task, focused diff, selected files, or repository context, and Jev returns structured per-dimension scores and confidence values for areas such as correctness, complexity, readability, modularity, changeability, testing, reliability, security, and documentation. When a previous evaluation is supplied, the plugin reports metric deltas, improvements, regressions, and weak dimensions rather than producing a blended overall score or prose diagnosis; the coding agent remains responsible for identifying causes and changing the code. It supports Claude Code, Codex, Cursor, and OpenCode, and is distributed directly from its GitHub repository rather than npm. The process keeps the API key locally and sends only explicitly supplied review context directly to the configured Jev API; it has no hosted backend, database, telemetry service, or author-operated proxy. The repository is licensed under MIT.
Jev Search is a web search application built by Search1API that uses TypeSafe's Jev to interpret plain-language requests, select search terms, sources, and time ranges, and rank results. It queries general and vertical search engines concurrently, merges results by URL, engine agreement, original rank, and Jev relevance scores, and returns links and snippets with editable filters rather than generated answers. The application streams results as search lanes finish, reports failed sources separately, and can be installed as a browser app; it requires a network connection for searches. The open-source application is MIT licensed and can be self-hosted on Cloudflare Workers with Search1API and a Jev provider such as TypeSafe, Vercel AI Gateway, or Cloudflare Workers AI.
jev-trader is an AI market-making bot for the Kuru MON-USDC order book on Monad. A TypeSafe Jev model observes the order book once per block and predicts whether to buy or sell over a configurable future horizon; each block, the bot cancels its resting orders and submits a post-only limit order one tick inside the touch, aiming to earn the spread when a taker fills it. Its hot loop reads the book with one RPC call and submits the transaction with one asynchronous raw-transaction call, leaving receipt processing, fee estimates, and vault checks off the critical path. It records block decisions, quotes, fills, positions, and profit-and-loss data, and exposes snapshots, history, and server-sent events through a Bun server. If the model misses the block, it holds and posts nothing; position or margin limits can redirect a quote to the opposite side. The repository supports a dry-run mode with simulated fills and a mock momentum heuristic, or live Jev decisions using a TypeSafe AI API key and private key. It includes a deployed dry-run dashboard endpoint and can be run with Bun.
kev is an open-source, local Jev-inspired decision model from Jared Palmer. It runs on a laptop and accepts a document or other state plus typed questions, returning calibrated probabilities rather than generated text. It supports yes/no (noul), multiple-choice, and ordered-score questions. The model uses a LoRA adapter and a readout head on a Qwen base model. It packs the state and all questions into one sequence, applies a block-causal mask so each question can see the state but not sibling questions, and performs one prefill pass without decoding. A pointer head scores each option against the question's decision token and applies softmax; the head is trained with cross-entropy on labelled outcomes. The local server exposes a TypeSafe System One-compatible API at POST /v1/systemone, allowing the official TypeSafe SDK to connect by changing its base URL. It also provides model information, option-order permutation, and separate-pass comparison endpoints, plus a local playground. The repository includes 0.5B, 0.6B, 4B, and 8B model checkpoints and recipes for local or Modal-based training and evaluation. Serving is tested on Apple Silicon, with larger checkpoints requiring bf16 on a 32 GB Mac. The server has no authentication and is intended for local use. The project is licensed under Apache-2.0; base models and datasets have separate licenses.
Laya is an Apache-2.0 Python package and encoder-model decision engine developed by Convai Innovations. It accepts text or JSON state and evaluates typed questions in a single forward pass, returning categorical choices with probabilities and confidence, ordinal scores, or calibrated Boolean probabilities rather than generated text. Its stated uses include routing, ticket triage, prompt-injection detection, moderation, phishing detection, and churn-risk classification. Laya includes English, multilingual, and typed-decisions checkpoints, plus a router that selects a checkpoint using explicit settings, detected script, and best-effort language detection. The checkpoints use reinforcement learning against strictly proper scoring rules (RLCD); the project also provides fine-tuning and temperature-calibration workflows. The multilingual checkpoint is intended for more than 100 languages, while the repository cautions that the base models can perform near chance on some zero-shot typed-decision tasks and that large choice sets are constrained by the option-token budget.
Lead is a deliberately non-production PostgreSQL text-search extension from PlanetScale for exercising TIN-compatible application SQL in development, testing, CI, and staging. It provides the TIN access method, TINQL parsing and tokenization, the `==>` search operator, scoring functions, and highlighting. Lead does not store search data in its index. Each scan adds all table blocks as candidates to a lossy bitmap, after which PostgreSQL rechecks visible rows for exact TINQL and MVCC behavior; scoring rescans and retokenizes the indexed column or expression. It is designed as a slow, correctness-focused substitute for TIN at small scales, is unsuitable for production workloads, targets PostgreSQL 17 and 18, and loads as a normal extension without shared preload libraries.
LocalJev is a TypeScript service that provides a local Jev-compatible decision API for applications using TypeSafe's typed decision interface. It exposes a POST /v1/systemone endpoint and translates typed Jev questions and application state into classification prompts for an OpenAI-compatible DiffusionGemma inference server. The service validates and retries malformed JSON, normalizes probability vectors, calculates Jev-compatible choices, expected scores, and entropy-based confidence, and returns the standard Jev response shape. Its probabilities are model-generated or self-reported rather than read directly from model logits, so the repository advises evaluating calibration for the intended workload. It runs with Bun and can use oMLX as the local inference server; the default LocalJev address is http://127.0.0.1:8080.
Mobile Jev is a standalone Android agent from droidrun that turns a written goal into device actions through the Mobilerun API, using TypeSafe's Jev to choose operations such as opening apps, tapping, entering text, scrolling, navigating, waiting, completing, or blocking. It observes the device, offers indexed controls and installed apps to Jev, validates the selected target against fresh screen data, executes the action, and observes the device again; it does not require an ADB connection. The executor resolves coordinates from observed bounds, rejects stale targets, records actions and execution traces, and verifies field values after text entry rather than relying solely on a model completion response. The project includes a local React studio with a live device stream, goal input, action timeline, task clock, model-latency measurements, stop and reconnect controls, and recent runs, along with a CLI for previewing or executing goals, inspecting devices, taking screenshots, profiling API timings, and issuing direct controls. It requires Node.js, pnpm, a Mobilerun Android device, and Mobilerun and TypeSafe API keys; it is intended for a single local operator, with recent runs held in memory. The repository is MIT licensed.
Model Context Protocol (MCP) is an open, standardized protocol layer for connecting large language models and AI agents with external data sources, hosted infrastructure tools, and other contextual data. It defines formats, metadata, protocol schemas, and APIs for sharing information and tools, attaching, referencing, and validating context such as documents, embeddings, and provenance, while supporting scoped authentication and permissions. MCP translates JSON requests from an agent into calls to service APIs, including CRM, container-management, and GKE capabilities, and can provide design-system context and related tools for generating consistent applications. The official project publishes its specification, documentation, and protocol schema; the schema is defined first in TypeScript and also provided as JSON Schema for broader compatibility. The protocol was created by David Soria Parra and Justin Spahr-Summers, is hosted at modelcontextprotocol.io, and is licensed under the MIT License.
NanoJev is an open-source 0.6B parallel-decision model and training pipeline based on Qwen3-0.6B. It accepts states, questions, and candidate sets in a single forward pass and returns complete probability distributions without output-token decoding. Shared decision heads support dynamic-choice distributions over supplied candidates, Boolean proposition probabilities, and ordered score levels with expected values. The repository includes data generation, distribution-loss training, evaluation, persistent serving, and browser replay tools. Its game demonstrations combine model judgments with controller code: maze controllers ask local safety questions and track collisions and explored paths, while Snake controllers filter immediate collisions, plan toward visible food, and use the model to break ties between remaining actions. Checkpoints and datasets are distributed through Hugging Face, and the service exposes repeated batched evaluations through a local HTTP endpoint.
OpenAI is an AI model provider whose language models are available through an API and can serve as one provider in multi-model routing architectures. The videos describe its models being used for AI replies and extraction, Amazon-review analysis and customer-service email generation, legal workflows, complex software development and debugging, and experimental or auxiliary agent tasks.
perch is a semantic code-linting CLI that checks source code against plain-English rules using Jev. It scans files and reports ranked findings with line numbers, severity, problem and method labels, and confidence scores; issues can be inspected individually with `perch issues` and rechecked after fixes with `perch check`. Custom rules are defined in file-scoped `perch.yaml` configuration using selectors such as `where` and `each`, minimum confidence thresholds, and an `ensure` statement. It also provides setup commands for Claude Code, Codex, pi, and Cursor, and can gate builds in CI.
procedural-film is an agent skill that turns a topic into a 30-second vertical film. It uses vanilla JavaScript to draw every pixel on a canvas and Web Audio to synthesize every sound, producing a self-contained HTML player and MP4 exports without media assets. An agent plans the film, dispatches parallel subagents to write shot scene files, and runs critic reviews across the shots. The repository includes a shot-type index, scene and music guides, planning templates, a rendering engine, and a monarch-butterfly reference film. It requires Node.js 20 or newer, ffmpeg, and Chromium for Playwright; the project is licensed under MIT.
Project Titania is a from-scratch large language model system covering a decoder-only transformer, compiler, instruction-set architecture, ISA simulator, and planned GPU hardware. It runs Qwen3-0.6B and expresses the model as GPU kernels for matrix multiplication, normalization, attention, and activation functions. The compiler lowers those kernels into Titania ISA programs, while the ISA simulator executes the instructions as the reference implementation of their behavior. Running `titania run --device isasim` compiles each model operation into a Titania kernel for its shape and displays the simulated kernel execution, including a warp's current disassembly. The repository also provides a CLI that can run the model on the CPU or simulator and includes a bash tool whose commands run without confirmation. The project is written to keep each layer readable and uses the MIT license. Its hardware layer is an RTL GPU design planned to progress through RTL simulation, FPGA synthesis, and eventual silicon; the current end-to-end model execution described by the repository uses the CPU and ISA simulator.
Rust is a systems programming language and toolchain developed by the Rust project. Its compiler, standard library, documentation, package manager and build tool Cargo, formatter rustfmt, linter Clippy, and editor support are maintained in the main repository. The language uses a rich type system and ownership model to enforce memory and thread safety at compile time, while targeting fast, memory-efficient software for critical services, embedded devices, and integration with other languages. Rust is primarily distributed under the MIT and Apache 2.0 licenses, with portions under BSD-like licenses.
SemIf is an independent open-source tool, formerly called OpenJev, for making runtime-defined semantic decisions with open language models. It accepts unstructured state, runtime-defined questions or criteria, and typed options, then reads the model’s native option logits to return conditional probabilities rather than generating an answer sentence, JSON, or running a decoding or parsing loop. It supports direct, serial, and shared-state execution, prefilling common state for evaluation of multiple criteria; results include option scores, timing, the exact model revision, and a prompt hash. It supports local GPU and CPU workflows, Apple Silicon MPS and MLX, and llama.cpp backends, and provides a browser-based WebGPU demo, reproducible fixtures and benchmark runners, calibration methods, and quality evaluations. The project is an independent reproduction of the interface pattern of TypeSafe’s closed Jev service, not its model or training, and is not affiliated with Jev or TypeSafe. The code is released under the MIT License.
Simple Jev is an open-model classifier and scoring server from Featherless AI. It accepts shared context or chat messages plus questions, then reads the model's next-token logits for predefined answer labels instead of generating a prose or JSON completion. The server constructs JSON responses for choice classification, ordered rubric scores, and truth or support judgments, including confidence and label distributions. Its Hugging Face Transformers implementation identifies the exact common token prefix across questions, evaluates that prefix once, reuses its KV cache for question-specific suffixes, and batches the suffixes before scoring the allowed answer tokens. The response-scoring code normalizes the selected logits; shared context is counted once for usage purposes, and no output tokens are generated. The API provides non-streaming /v1/classifier and /v1/systemone endpoints, with local execution supported through Python, PyTorch, and Transformers. A public demo API is available, while production deployments can use Featherless paid plans or run the server themselves. The repository also includes RFDT, which fine-tunes models directly on allowed answer-token logits using the same prompt structure.
Splash is a local inference engine for Apple silicon developed by Inco AI. It serves selected language models to coding agents and clients compatible with the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs, with streaming, tool calls, JSON Schema output, image input, and inline PDF support. Its runtime uses model-specific fused Metal kernels, weights packed for those kernels, a dedicated DFlash 2 draft model for speculative decoding, and a startup-computed memory plan for context, KV cache, and batching. The served model packages include the target model and its draft model; plain MLX or Transformers checkpoints are not supported. Splash runs locally on macOS, binds to a local HTTP server by default, and can be installed through Homebrew. The project is licensed under Apache-2.0, while model weights retain their own licenses.
An open-source tax-document page classifier built with TypeSafe Jev. It extracts text from PDF pages, then uses decision-model requests to classify each page by form and page kind, returning probability distributions, a form identifier, confidence scores, and a gate indicating whether the result meets the configured confidence threshold. Its generated JSON criteria file describes IRS forms using data extracted from IRS form PDFs; a second request handles schedules for selected corporate and foreign forms. The package includes PDF helpers based on Poppler, supports text-layer input, and requires a TypeSafe API key for the Jev backend. It does not train or host a model, is limited to English federal-form classification, requires OCR for scanned pages, and is distributed under Apache-2.0 with separate licensing for IRS-derived data.
typesafe-computer-use is an open-source macOS computer-use tool that drives a Mac toward a plain-English goal. It captures the frontmost window, reads text with OCR, collects labelled controls from the macOS accessibility tree, parses dates, and sends a structured list of possible actions to the TypeSafe decision model instead of sending screenshots to a frontier model for every step. The loop chooses actions such as clicking, scrolling, browser navigation, waiting, or pressing keys; a writing model is called only for free-text entry, proposed URLs, and the final answer, while passwords are never typed. It supports dry runs, confidence and step limits, annotated captures, detailed action and probability logs, and offline replay of saved screenshots. The repository targets macOS 14 or newer with Python 3.12 or newer, requires a TypeSafe API key, and is licensed under the MIT License.
TypeSafe Mario is an experimental controller that uses TypeSafe's Jev model to play the original Super Mario Bros. from structured emulator state rather than screenshots. A parser converts emulator telemetry and RAM into object-centric JSON describing Mario's motion and jump trajectory, nearby enemies, terrain, response timing, recent control results, and episode progress; Jev then selects one of a small set of legal NES controller actions, after which the emulator advances and the loop repeats. The system also runs separate Choice, Noul, and Score judgments over each state: Choice selects the next controller macro, Noul estimates whether a forward jump is useful, and Score measures immediate danger for visualization. It provides a demo state inspector, a live dashboard with action probabilities, confidence, latency, jump probability, danger score, reward, and parsed state, plus JSONL decision logs containing model state, debug state, timing, and game outcomes. It requires Python 3.13 or newer, a TypeSafe API key, and a lawful local emulator and game setup; the repository does not include the Nintendo ROM or other copyrighted game data.
An Anthropic reference release containing 36 drop-in inference-optimization kits for open protein- and genomics-machine-learning tools, covering structure prediction, cofolding, binder and sequence design, protein-language-model inference, and genomic prediction. Each kit carries a pinned upstream release in a stock directory and applies optimizations under a named mode without changing the upstream calling interface. Modes include off for the unmodified upstream tool, exact for identical outputs with faster inference, fast for documented numerical differences, and big for lower peak GPU memory; some kits also support splitting a prediction across multiple GPUs. The kits share runtime code for mode resolution, determinism, memory handling, multi-GPU execution, and GPU kernels, and can be installed through Docker, Apptainer, or a pinned Python environment. The repository is a non-maintained reference release accompanying Anthropic's biomolecular-modeling blog post; its original code is under Apache License 2.0, while the carried upstream projects and model weights have their own licenses and terms.
The Web Audio API is an audio-programming API used to generate sound for procedural videos.
Searchable transcript of GitHub Trending Today #50: fast-jev-compaction, SemIf, jev-trader, jev-search, pg-jev, jev-review — Github Awesome (15:07). Search for a phrase, then click its timestamp to jump straight to that moment in the video.
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00:00 This is GitHub trending today number 50. Jev keeps showing up in the feed, so today's lineup leans heavily into it. From local decision models to tools that review code and control devices. Let's get into it. Fast Jev compaction fixes the thing that's annoying about normal context compaction. It summarizes and summaries lose the exact file path or error message you needed two turns later.
00:22 This one never rewrites anything. It scores every tool call and result with Jev and only deletes the ones scored as no longer needed, keeping your actual text untouched. Compositor is an open- source Mac image editor built around compositing with Photoshop style shortcuts. You can combine layers with masks and blend modes, then adjust color through editable adjustment layers.
00:47 Resizing a layer preserves its original resolution, so shrinking it doesn't throw away detail. contentaware fill and a clone stamp handle cleanup. SEM if does the same trick as type safe's jev reading probabilities off option tokens directly except it runs on open models you can actually download instead of a service with a weight list ask a model to pick between 21 options the normal way generate JSON and parse it and on their benchmark that took 5.3 seconds on an RTX3090 reading the typed logits directly did it in
01:24 about 1 second five times faster Jev trader is a market maker built to fit an entire decision inside one monad block. Most bots blow their block window doing gas estimation and synchronous sends on every order. So by the time it posts, the price already moved. This one hardcodes gas limits and static fees and cuts everything else off the path. Decision to order runs in about 100 milliseconds.
01:50 80 of that just inference. Jev Ultraast clicks through a web page without generating a single coordinate or selector. Most browser agents screenshot the page, feed it to a vision model, and wait while it writes out where to click step after step. This one reads the DOM directly, builds an index table of clickable elements, and makes two decisions, action and target, in one network round trip.
02:16 Jev uses AI to choose where to search and rank what comes back without writing a generated answer. Describe what you need and Jev selects search terms, sources, and a time range with filters you can override. Results arrive as links and snippets with visible relevance scores while duplicate URLs get merged. Self-hosting still requires type safe and search one API keys and each search can trigger multiple billable calls.
02:44 Tax.classifier identifies tax forms page by page, returning labels and confidence scores instead of generated explanations. It extracts PDF text and sends it to Typesafe's Jev model using form descriptions derived from IRS documents. Your pipeline can route low confidence results for review. It identifies federal forms in English, but scanned pages need OCR first.
03:06 Without a text layer, they're treated as blank. Kev is a local open- source standin for Typesafees Jev that runs entirely on your laptop. Built on quen 2.5b, it reads a document once and answers multiple questions in parallel, returning probabilities instead of generating text. Each question can see the document but not the other questions. Its local server works with Typesafe's SDK, so you can reuse that interface.
03:37 NanoJV is a nano replica of Jev, 600 million parameters that scores every possible move in one forward pass instead of generating a token at a time. You supply states, questions, and candidate answers, then batch decisions into one forward pass. The repository includes the training pipeline and browser replays of maze and snake tests. Abot Recon builds a 3D reconstruction from video while keeping just 12 frames of local context.
04:04 It estimates geometry and camera movement frame by frame, then combines those estimates into a global point cloud and trajectory. that keeps the model's working memory from growing with video length. Optional loop closure can refine the trajectory when you revisit a location. Typesafe computer- use skips screenshotting a frontier model for every single click.
04:28 Most computer use agents send a screenshot to something like Opus every step, wait five plus seconds, and burn real money doing it. This one reads the screen deterministically. instead OCR plus the accessibility tree plus date plus date parsing and only calls a writing model when it actually needs to type something. PGEV adds plain language conditions to SQL so you can filter support tickets by frustration or classify them by department without creating embeddings.
04:59 It sends row data to Typesafe's Jev API in batches and caches answers within the session letting you change thresholds without repeating those calls. It needs super user access and PostgreSQL's untrusted Python extension which rules out many managed database hosts. ESP screens turns a cheap ESP32 touchscreen into a real smartome control panel without writing a line of YAML.
05:26 The alternative is either an expensive wall tablet or hand coding ESP home for a weekend just to get your lights on a screen. This one's drag and drop straight from Home Assistant. Up to 48 tiles across eight pages. lights, climate, blinds, media, even history graphs. Aside code mode lets an agent search files, filter results, and batch browser work inside one JavaScript call.
05:48 Instead of filling the conversation with entire files or pages, it can return just matching paths or extracted fields. Local search uses RIP Grep while browser batches run through a site's native ripple. The gain comes from fewer tool round trips and smaller outputs, not a faster search engine than rip grip itself. Jev review is honest about what an AI code reviewer actually is, a prompt worth investigating, not proof of a bug.
06:18 Most AI review tools hand you a confident paragraph and let you sort out later whether it's real. This one uses bounded judgment calls with explicit thresholds instead of a blackbox explanation, checking correctness, security, reliability, compatibility. Lia turns text or JSON into decisions without generating an answer sentence. You define questions with choices, yes or no outcomes, or ordered scores.
06:42 And its encoder models return probabilities for routing tickets or checking content. A built-in router selects English, multilingual, or task focused checkpoints and loads them when needed. HFGen lets you replay marketmaking back tests frame by frame against recorded Binance futures data. Its Rustr runner uses HFT back test while the dashboard shows the order book, estimated Q positions, latency, and simulated fills.
07:12 You can inspect why orders were filled or rejected instead of staring at a final score. It never places real orders and the simulation excludes market impact and partial fills. Herder projects gives you one coordinator conversation for work spread across multiple coding agents. After you approve its proposed tasks, each worker gets a separate work tree and branch with shared instructions and project memory.
07:37 The sidebar separates finished work from threads waiting for your input so you don't have to check every conversation. Foreman watches a coding agent while it works using Jev to assess progress, test coverage, and whether human input is needed. A separate Python policy decides when to steer the active codec session, stop a stuck worker, or launch verification.
08:00 The worker keeps running during those assessments. It's upfront about being an experiment, though. Jev's accuracy for this job is unproven and still needs calibration. Flyion is a wallet tracker that actually publishes how wrong it is. Most of these trackers rank the top trader as whoever got a lucky airdrop or bridge tokens dumped into their wallet.
08:22 And none of them let you check a real hit rate. Flyion writes every call to a hashchain ledger before the result exists, then settles it later against the first price once the window closes. Typesafe Mario lets Jev choose controller inputs from structured game data. Rather than screenshots, the program reads emulator memory and turns movement, terrain, and nearby enemies into JSON with timing calculations handled in code.
08:50 A live dashboard shows action probabilities and response latency, while logs preserve each decision for analysis. Procedural film makes a 30-se second vertical video with zero stock footage, zero AI generated clips. Every pixel is just JavaScript drawing on a canvas and every sound comes out of the web audio API. You hand it a topic and an agent writes the shots, critiques its own work across multiple review passes and renders the whole thing to an interactive HTML file plus an MP4.
09:24 Titania runs Quen 30.6B through its own compiler, instruction set, and simulated GPU, so you can follow how model operations become individual instructions. A live panel shows the executing kernel and disassembly while it generates text. The physical GPU is still planned, including FPGA and eventual silicon stages. One practical warning, its chat interface includes a bash tool that runs commands without asking for confirmation.
09:53 OpenJV SG lang serves a Jev compatible API using Quen with no requests sent to Typesafe. It scores answer options from first token probabilities instead of generating explanations. and reuses cached context across parallel questions that gives you yes or no decisions, choices, and ordered scores through one interface. The included deployment uses a B200 GPU on modal lead lets you test tin compatible text search queries in Postgress without running the production tin engine.
10:23 It supports query parsing, scoring, and highlighting so application SQL can run in development and CI. The unusual part is its index. It stores no search data and sends every table page back to Postgress for checking. That favors correctness over speed, making lead a testing substitute, not a production search engine. Expo GPT live is a voice app template that lets you interrupt AI replies and keep conversations running in the background on iPhone.
10:52 It combines GPT Live 1 with web search, a weather tool, and animated personas while keeping your OpenAI key on the server. You'll need a native build rather than Expo Go. Before making the backend public, replace its shared development token with proper user authentication. Jev review gives coding agents separate quality scores instead of a written code review.
11:17 Your agent submits a focus diff through MCP, gets feedback on dimensions like correctness and complexity, then compares scores after changes. Jev doesn't explain what caused a weak score. The coding agent still has to investigate and fix it. The plugin runs locally, but submitted code goes to Typesafe's API for evaluation. Anthropic's uplifting biomolelecular modeling packages inference optimizations around pinned research tools without changing their usual calling interface.
11:46 Its modes distinguish output preserving optimizations from those allowing small numerical differences or prioritizing lower GPU memory use. Each kit reports which optimizations activate and refuses unsupported configurations rather than silently falling back. Mobile Jev turns a written goal into Android actions through mobile run without an ADB connection.
12:11 Jev chooses an operation and target in one request while code checks that the target is still valid before acting. A live studio shows the phone action history and latency. Text entry copies exact wording from your goal rather than generating it. And a model saying done still needs verification against the phone's actual state. Bashka checks shell installers before you run them, flagging patterns like credential theft, destructive commands, and unverified downloads.
12:40 It can follow scripts that fetch other scripts and records installed binaries so you can review, update, or remove them later. The useful distinction is inspection, not isolation. Static checks can misbehavior, and a server can change what it serves between review and execution. Splash runs language models locally on Apple silicon using kernels and draft models tailored to each supported model.
13:09 The draft proposes blocks of tokens that the main model checks in parallel while startup calculates a memory budget for your Mac. It exposes open AI and enthropic compatible APIs for existing clients. The trade-off is flexibility. Ordinary MLX or transformers checkpoints won't load. Simple Jev is for when you don't need a model to write an essay just to pick an answer.
13:32 It reads the loits directly instead of generating text. So choice, score, and truth judgments come back as JSON. No parsing required. It caches the shared prefix across questions. The maintainers say four questions on one document dropped from 4200 tokens to,200. Local Jev lets apps using Typesafe's decision interface talk to a local language model through a standard chat endpoint.
13:58 It translates questions into classification prompts, validates the return JSON, and calculates choices and scores in code. The important difference, these probabilities are numbers the model writes, not values read directly from its internal logits. Abide turns your project instructions into checks that run after coding agent edits. And at the end of each turn, it sends rules and changed code to Jev, then asks the agent to repair likely violations, including judgment calls a llinter might miss.
14:30 Each verdict points back to the original instruction. It's feedback, not a hard gate. Without a key or network connection, edits go through unchecked. Perch checks code against plain English rules using Jev, so you can express requirements like keeping environment variable reads out of individual methods. You scope rules to files, choose what gets checked, and set thresholds in YAML. Scans produce ranked findings with line numbers and confidence scores, and you can recheck an issue after a fix.