Introducing Respan Router, powered by Span-01. Automatically routes every... request using task context, model capabilities, and cache-aware cost estimates behind 1 model id: 𝐬𝐩𝐚𝐧-𝐫𝐨𝐮𝐭𝐞𝐫 - 13% lower latency than jev-router on τ-bench airline - 37% cheaper than the most accurate single model, at matching accuracy - 0% processing fee on Respan Gatewayshow more

Built a model router with Jev by TypeSafe AI.... Jev decides what model fits your request best and the request is sent to that model.show more

Meet Auto, our upgraded model router 🔀 Frontier quality... at 90% lower cost. It matches each task to the right model, balancing quality, speed, and cost. And if it gets stuck, it changes course.show more

Doc-OCR router using Jev TypeSafe AI A Jev-powered router... that looks at a PDF page by page, decides which pages actually need OCR, extracts the rest locally. Result: save cost on # OCR pages + speedshow more

You can now use Jev right inside Claude Code... 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.show more

The opposite of token-maxxing isn’t giving up on AI.... It’s using the right model for the right task. The new Auto router in Notion handles that for you.show more

Monitor and control your AI spend on every provider... on Our early users save 40% on average. Every week, the price-intelligence-latency frontier shifts, and we expect this trend to continue. Tradeoffs between latency, reasoning, cost, service tier, open source and closed source models are shifting constantly. Router sends every request to the model that's actually best for the task and helps you control what tokens you buy. We benchmark it against real work: ~40% lower cost for the same outputs. Today we're opening it to everyone. Two lines of code or just change your base URL. No Ramp account needed. Free through 2026, first $26 on us. Get an API key today atshow more

Opus 5 is the best Claude model on ReactBench... Better at frontend code than Claude Fable 5 and >2x cheaper (!!)show more

50% cheaper Claude inference with just one line of... code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.show more

$TSLA 🇰🇷 The Model 3 RWD and Model Y... L sold in Korea are cheaper than any other country. • Model 3 RWD ($28,000~) • Model Y L ($43,000~)show more

Introducing the AI Registry Discover all models in the... AI SDK AI Gateway: 🔍 Filter by: - Name - Input/output modalities - Context length - Output size - Providers 📄 View model details ⚖️ Compare model vs. model Open-source, link below!show more

Introducing $JTVO Staking Stake JTVO to fund GPUs that... run open-source models for you: automatically, and fully private. Every other model is still one key away on the Jatevo gateway. live on this end of the weekshow more

NVIDIA open-sourced a 600M model that transcribes 40 languages... in real-time at 80ms latency and it costs $0. that's faster than you can blink. across mandarin, arabic, hindi, portuguese, tagalog, whatever,from a SINGLE checkpoint. → 17x more concurrent streams than buffered ASR on the same H100. → punctuation + capitalization built-in. no post-processing. → runs on your own GPU. no API bill 100% Open Source.show more

AMAZON SENIOR DEVELOPER BUILT A CONTEXT PIPELINE THAT DECIDES... WHAT THE MODEL EVEN GETS TO SEE Most teams still dump the entire codebase into every prompt and hope it sorts itself out. He ranked every file by relevance to the task instead of how recently it was touched. A router decides how much context each task earns, a typo fix pulls three files, a full rewrite pulls the whole module. Whatever survives gets compressed to the exact lines that actually matter, so nothing bloats the window with dead weight. Token cost per finished task dropped the moment the model stopped reading dead weight just to fix one function. See how the four stages work together below👇show more

Routing for long-horizon coding agents is a big deal.... Not Diamond just announced a model router that works natively with Claude Code. This is huge. It picks the model and reasoning effort before each turn in a session, runs through a privacy-preserving local proxy, and your requests still execute through your own gateway. In their benchmarks, it approximates Opus 4.8 Xhigh quality at 39 to 61 percent lower cost.show more

The next step after Jev: A decision model like... Jev bills and computes the full context on every call, so a 40-message chat checked after each message processes the early messages 40 times. Devs just built Sage2, a streaming decision model that computes each token of a conversation once. The setup is closer to a database cursor than an API call. A session holds the context on the server, and new messages are appended to it, not re-sent. The cached computation over the earlier context is reused. Each new message adds only its own tokens to the cost, and every registered question is re-evaluated against the full history. Each question returns a typed answer with a calibrated probability, so thresholds and routing stay in plain application code. On progressive context, this makes decisions 3x faster, with responses in tens of ms. Sage2 is built by Levanto Labs, the team behind the first decision model, launched before Jev. The video below depicts the model in action on a car dealership chat, and I partnered with the team to put this together. More details below.show more

HOLY MOLY: Aikido got GPT-6 Astra in advance to... run it on our Cybersecurity benchmark, it crushed EVERY other model! - At pass@3 it rediscovered 29/32 CVEs, the highest recall we've ever recorded and 4 more than GPT-5.6-Sol - Even at pass@1, it has 75% recall. The model is VERY consistent - The performance however come at a high price (literally). The three runs cost us almost $4,000 Astra is now the #1 model on the benchmark Debarshi and I built, and by a LOT 1/3 🧵show more

This is absolutely f*cking insane. you can put a... $0.042-per-million-token model in front of Claude and let it decide what Claude ever has to see. that's Jev signal routing, and the trick is stupidly simple: Jev takes the first look at everything. Claude only gets the hard cases. instead of: data -> Claude -> tokens burnt $ it's: data -> Jev -> only the unsure ones -> Claude and here's the part people miss. every Jev answer comes with a probability. that's your router: -> Jev is sure? its answer is final -> Jev hesitates? the case goes up to Claude set the cutoff once, by testing on your own data, not by guessing. 3 repos that already do the routing for you: -> duckdb-jev: route your data. Jev labels every row straight from SQL, up to 1,000 calls in one request -> quicksilver: route your files. Jev checks all 187 files in parallel and Claude gets the 4 that matter. ~2.4k tokens instead of ~26k -> jev-router: route your models. before every message Jev decides simple or hard and sends it to the fast model or the strong one. works with your normal Claude login where this pays off right away: -> support tickets -> your inbox -> lead lists -> messy spreadsheets -> comment moderation on a public benchmark Jev labeled 1,000 rows for $0.015. Claude charged $0.80 for the same job. faster. cheaper. way fewer tokens burned. Jev sorts the mail so the genius only opens what matters. Claude thinks. Jev routes. code does the job.show more

everyone on x: "astra is cooked. opus 5.5 is... agi" me: i can't switch tools every other week when a new model drops Ramp router: you shouldn't have toshow more

With iOS 27 Apple introduced its most powerful on-device... model: AFM 3 Core Advanced It’s a 20 billion parameters model activating only between 1 to 4 billion parameters depending on the request Here’s the model running on iPhone 17 Proshow more