AI Coding Live News

Jev maker TypeSafe AI raises $870M Series A at $7.5B valuation led by a16z, less than a month after Jev's launch

TypeSafe AI announced an $870M Series A at a $7.5B valuation on Oct 9, led by a16z with Sequoia and existing investor DCVC; Martin Casado joins the board. Its 'System One' decision model Jev launched Sept 15 and returns calibrated, typed decisions instead of text. TypeSafe says a third of the Fortune 500 use it; a16z's post says 25% — the figures differ.

Jev maker TypeSafe AI raises $870M Series A at $7.5B valuation led by a16z, less than a month after Jev's launch

Key Takeaways

  • $870M Series A at a $7.5B valuation, led by a16z with Sequoia and DCVC; Martin Casado joins the board
  • Jev launched Sept 15, 2026 — roughly 3.5 weeks before the round (TechCrunch)
  • a16z claims Jev generated 1 trillion tokens within 3 days of launch
  • Enterprise adoption claims differ: TypeSafe says a third of the Fortune 500, a16z says 25% — both self-reported
  • TypeSafe promises more machine-native models and enterprise features; docs at docs.typesafe.ai
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llama.cpp ships Decision Models: /v1/systemone scores options in one forward pass (Julia-1, Laya, Kev-4B, lev, OpenJev)

On 2026-10-02 ggml-org announced llama.cpp server support for Decision Models via /v1/systemone (PR #29818, merged 2026-10-02T09:56Z). Send a state plus typed questions; get option probabilities in one forward pass. Day-0 models: Julia-1, Laya, Kev-4B, lev, OpenJev (vision). Cloudflare Clef is planned next.

llama.cpp ships Decision Models: /v1/systemone scores options in one forward pass (Julia-1, Laya, Kev-4B, lev, OpenJev)

Key Takeaways

  • Official post 2026-10-02 + merged PR #29818 (2026-10-02T09:56Z, +2139/−15)
  • Endpoint POST /v1/systemone with choice/score/noul; probabilities + confidence; output_tokens=0
  • Day-0 GGUFs: Julia-1 / Laya / Kev-4B / lev / OpenJev; median ~3–43 ms/question on RTX PRO 6000 (blog table)
  • Distinct from Ollama SystemOne & Cloudflare Clef hosting; Clef listed as next in llama.cpp
  • Start: llama serve -hf ggml-org/Kev-4B-GGUF then curl /v1/systemone
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Linked Models & Agents:
⚡ Julia 1⚡ laya⚡ Kev 4b⚡ lev⚡ openjev🌐 ggml-org🌐 huggingface
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Comet@comet-ml·
👀53
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Comet Opik 2.2.84: online eval rules call Jev via OpenRouter Decisions API; queue automation + provenance

Comet Opik 2.2.84 (2026-09-29) lets online LLM-as-a-Judge rules call TypeSafe Jev end-to-end via OpenRouter POST /api/alpha/decisions (not chat/completions). Each Boolean metric becomes a yes/no question answered in one call; score 1 if probability ≥0.5, with Probability: 0.93 in the reason. Backend adds OpenRouterDecisionsClient (workspace OpenRouter key, 429/5xx retries) for ~typesafe/jev-latest / typesafe/jev-1.13. Also: Annotation Queue automation + item provenance. ~8 commits / 104 files vs 2.2.83.

Comet Opik 2.2.84: online eval rules call Jev via OpenRouter Decisions API; queue automation + provenance

Key Takeaways

  • Release 2.2.84; ~8 commits / 104 files vs 2.2.83
  • PR #8540: online judges call Jev via OpenRouter Decisions API
  • Boolean metrics → yes/no questions; score 1 if p≥0.5; reason shows Probability
  • Models: ~typesafe/jev-latest, typesafe/jev-1.13
  • Queue automation + item provenance; trace stats / thread_id fixes
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Ollama 0.35.0: Decision Models via /v1/systemone (Jev); Nimble & Tev1 return choices, probabilities, confidence

Ollama shipped v0.35.0 on 2026-09-28 (GitHub prerelease): Decision Models via TypeSafe’s Jev API at /v1/systemone. Instead of free text, models answer choice/noul/score questions with options, probabilities, and confidence—for ticket triage, model routing, and classification. First models: Nimble (Bespoke Labs) and Tev1 (Together AI). ~7 commits / 66 files vs v0.34.4; Settings no longer blocks on discovery; macOS update menu and stalled MLX downloads fixed.

Ollama 0.35.0: Decision Models via /v1/systemone (Jev); Nimble & Tev1 return choices, probabilities, confidence

Key Takeaways

  • v0.35.0 (prerelease) adds Decision Models at POST /v1/systemone (TypeSafe Jev)
  • Question types: choice (with probabilities), noul, score — not free-text generation
  • First models: Nimble (Bespoke Labs) and Tev1 (Together AI) on ollama.com/library
  • ~7 commits / 66 files vs v0.34.4; Settings/macOS update/MLX download fixes
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GPT Researcher 0.16/v3.7.0: Jev usefulness context filter beats embeddings by 59% relevance

GPT Researcher shipped v3.7.0 on 2026-09-26 (PyPI gpt-researcher 0.16.x). The context filter now defaults to TypeSafe Jev, scoring passage usefulness for each sub-query instead of embedding similarity. On a 28-task replay with identical sources, kept-passage relevance is 73% for Jev vs 46% for embeddings (~59% relative lift). Without TYPESAFE_API_KEY it falls back to local BM25—no embedding provider required. Also requires Python 3.12+ and adds a retriever plugin entry point.

GPT Researcher 0.16/v3.7.0: Jev usefulness context filter beats embeddings by 59% relevance

Key Takeaways

  • Shipped: v3.7.0 / PyPI 0.16.1 — pip install -U gpt-researcher
  • Official 28-task replay: Jev kept-passage relevance 73% vs embeddings 46% (~+59%) at similar cost
  • CONTEXT_FILTER=auto uses Jev with TYPESAFE_API_KEY else BM25; Jev failures fall back to keyword
  • Chunking: 50k page cap, 1k/100 overlap; keep ≤10 chunks (Jev/embeddings) or ≤25 (keyword) per sub-query
  • Requires Python 3.12+; retrievers via gpt_researcher.retrievers entry points
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Confident AI@confident-ai·
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DeepEval 4.2: Jev for LLM-as-judge decisions at ~$0.042/M input tokens and ~100ms

Confident AI’s deepeval 4.2.x (notes for 4.2.2/4.2.4; PyPI at 4.2.6) wires TypeSafe Jev into LLM-as-a-judge: evaluation LLMs still handle language tasks, while closed verdicts go to System One for typed decisions with calibrated probabilities. Claimed decision-stage pricing is about $0.042 per million input tokens with no output charge, ~100ms typical latency, and batched independent questions. Faithfulness, GEval, DAGMetric, and Classifiers are integrated; enable via deepeval set-mode experimental plus typesafe-sdk—missing keys fail loudly instead of silent fallback.

DeepEval 4.2: Jev for LLM-as-judge decisions at ~$0.042/M input tokens and ~100ms

Key Takeaways

  • Notes: python-v4.2.4; blog Introducing Jev for Evals; PyPI deepeval 4.2.6
  • Official: ~$0.042/M input tokens for decisions, no output charge; ~100ms typical; batch independent questions
  • Faithfulness/GEval/DAGMetric keep language work on the judge LLM; closed verdicts use Jev Noul/Choice/Score
  • New Classifiers for categorical labels (e.g. RefusalClassifier)
  • Opt-in: deepeval set-mode experimental + typesafe-sdk + TYPESAFE_API_KEY (loud failure if missing)
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LiteLLM 1.102.1: Claude Code safeguards on stable + TypeSafe Jev router/guardrails backported

BerriAI shipped LiteLLM v1.102.1 on 2026-09-23 (PyPI litellm 1.102.1). Versus 1.102.0 it backports Anthropic/Bedrock/Vertex safeguards→safeguard_results passthrough so Claude Code Auto mode through a LiteLLM gateway can show Auto mode server: Enabled, and cherry-picks seven TypeSafe Jev changes (passthrough, complexity router, relevance compaction guardrail, auto-router classifier, OpenRouter jev-1.13 pricing, etc.) from the 1.103 RC onto stable/1.102.x. Docker images keep cosign verify instructions.

LiteLLM 1.102.1: Claude Code safeguards on stable + TypeSafe Jev router/guardrails backported

Key Takeaways

  • Shipped: v1.102.1 / PyPI 1.102.1
  • Claude Code: gateway forwards safeguards and returns safeguard_results (#42538) so Auto mode server can stay Enabled
  • TypeSafe Jev: seven PRs backported to stable/1.102.x (#42595)
  • Deploy: pip install litellm==1.102.1; cosign-verify ghcr.io/berriai/litellm:v1.102.1
  • Docs: Proxy Quick Start + Anthropic provider pages
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Linked Models & Agents:
⚡ Claude⚡ TypeSafe Jev⚡ jev-1.13🤖 Claude Code🌐 berriai🌐 Anthropic🌐 openrouter

simonw/llm 0.36: GPT-6 Sol/Luna catalog + supports_conversation=False for plugins

llm 0.36 (2026-09-22) adds gpt-6-sol/gpt-6-luna, lets plugins set supports_conversation=False (raises ConversationNotSupported; first used by llm-typesafe), and wraps reasoning traces in <details> in llm logs Markdown.

simonw/llm 0.36: GPT-6 Sol/Luna catalog + supports_conversation=False for plugins

Key Takeaways

  • Built-in gpt-6-sol and gpt-6-luna (#1702)
  • Plugins may set supports_conversation=False → ConversationNotSupported (#1692)
  • First consumer: llm-typesafe for Jev/TypeSafe models
  • llm logs wraps reasoning in HTML details (#1701)
  • Upgrade: pip install -U llm==0.36
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AG2 1.1.0: TypeSafe Jev decision-only agents + reserved context strip across transports

AG2 v1.1.0 (2026-09-24) adds TypeSafe Jev decision-only agents via TypeSafeConfig + response_schema, and strips reserved ag:/a2a: context keys on A2A/AG-UI/A2UI/NLIP to block remote pre-approval of gated tools.

AG2 1.1.0: TypeSafe Jev decision-only agents + reserved context strip across transports

Key Takeaways

  • Decision agents: TypeSafeConfig + response_schema; bool/Enum/IntEnum as question shapes; probs on ModelMessage.metadata
  • Jev rejects tools and streaming; invalid schemas raise locally before the wire
  • Install: pip install "ag2[typesafe]"; see TypeSafe Jev blog + docs.ag2.ai
  • Security: strip reserved ag:/a2a: prefixes both ways on A2A/AG-UI/A2UI/NLIP
  • Also: MessageEnqueued, MCP required-arg validation + JSON-RPC codes, event retarget fix
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Pydantic AI 2.50.0: DecisionModel protocol, named route selection, Gemini 3.8 Live realtime

Pydantic AI 2.50.0 (2026-09-25) adds DecisionModel (TypeSafeModel implements it), named route questions with optional decision_route_threshold, Gemini 3.8 Live realtime, durable-context hooks, and audio_seconds usage pricing.

Pydantic AI 2.50.0: DecisionModel protocol, named route selection, Gemini 3.8 Live realtime

Key Takeaways

  • DecisionModel base + TypeSafeModel implementer; ask routes by name (#8696, #8733)
  • Opt-in decision_route_threshold replaces tool-call lean; ModelSelectionContext.prompt (#8737/#8739)
  • Gemini 3.8 Live realtime + RequestUsage.audio_seconds (#8393/#8427)
  • decide spans + RunContext.in_durable_context (#8698/#8723)
  • Upgrade: pip install -U 'pydantic-ai==2.50.0'
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DSPy 3.4.0: Jev via TypeSafe (Noul/Choice/Score), ReAnchor calibration, native lm15 engines

DSPy 3.4.0 (2026-09-25) integrates Jev through TypeSafe with experimental Noul/Choice/Score decision types and ReAnchor calibration; the LM layer prefers native lm15 engines, adds LocalInterpreter and async ReActV2. 3.4 is the LM transition release; 3.5 is the migration deadline.

DSPy 3.4.0: Jev via TypeSafe (Noul/Choice/Score), ReAnchor calibration, native lm15 engines

Key Takeaways

  • Jev via TypeSafe: Noul/Choice/Score with probability evidence (#10463)
  • ReAnchor fits thresholds/cuts/weights against your metric (#10475)
  • engine=auto prefers native lm15; register_provider for compatible HTTP
  • LocalInterpreter for trusted CPython (not a security sandbox); RLM interpreter_factory= kw-only
  • 3.4 transition → 3.5 migration deadline; see LM migration guide
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OpenClaw 2026.9.6: managed updates, restart recovery, 30-day Usage, plus Opus 5.5 / GPT-6 Sol·Luna / Grok 4.7

OpenClaw shipped v2026.9.6 on 2026-09-23 (macOS DMG rebuilt 2026-09-24 after launch crash #156861): clearer managed-update outcomes, restart recovery for unfinished work, complete 30-day Usage reporting, a GitHub reader, remote workspace Files/Memory/Skills, live meeting notes, optional TypeSafe Jev Decision Models, and chat models Claude Opus 5.5, GPT-6 Sol/Luna, and Grok 4.7. Scale: 2,614 PRs, 178 direct commits, ~350 contributors.

OpenClaw 2026.9.6: managed updates, restart recovery, 30-day Usage, plus Opus 5.5 / GPT-6 Sol·Luna / Grok 4.7

Key Takeaways

  • Verified scale on docs: 2,614 PRs / 178 commits / ~350 contributors
  • Managed updates + restart recovery; Usage covers a full 30-day window
  • GitHub reader; remote workspaces gain Files/Memory/Skills; live meeting notes
  • Decision Models (TypeSafe Jev/local) + Opus 5.5, GPT-6 Sol/Luna, Grok 4.7
  • Install [email protected]; macOS users hit by #156861 should reinstall the rebuilt DMG (#156881)
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Pydantic AI 2.49.0: GitHub Copilot OAuth device flow, BoolCriteria, GPT-6 on Bedrock

Pydantic AI shipped v2.49.0 on 2026-09-24 (PyPI aligned). Highlights: GitHubCopilotOAuthFlow device auth, BoolCriteria for typed yes/no fields, RealtimeSession.wait_for_reply(), plus Bedrock Converse fixes for gpt-6-sol/gpt-6-luna/gpt-6-astra and dropping unsupported temperature/top_p on GPT-5.6/GPT-6.

Pydantic AI 2.49.0: GitHub Copilot OAuth device flow, BoolCriteria, GPT-6 on Bedrock

Key Takeaways

  • Upgrade: pip install -U 'pydantic-ai==2.49.0' (PyPI)
  • GitHubCopilotOAuthFlow device authorization (#8618)
  • BoolCriteria clarifies bool/Enum yes-no semantics (#8586)
  • RealtimeSession.wait_for_reply() (#8514)
  • Bedrock: allow gpt-6-sol/luna/astra; strip temperature/top_p for GPT-5.6/GPT-6 (#8656/#8668)
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TypeSafe co-founder shares coding-agent design notes: routing, compaction, subagents, skills, and MCP

@CompleteSkeptic (Diogo Almeida, TypeSafe; RLHF background) published public design notes on TypeSafe × coding agents (Google Doc), covering routing, context compaction, subagents, skills, and MCP tradeoffs, and invited the community to experiment. The post drew ~930 likes / ~56k views the same day.

Key Takeaways

  • Public Google Doc on coding-agent routing, compaction, subagents, skills, and MCP.
  • Authored by a TypeSafe principal; explicitly invites community experimentation.
  • Same-day high engagement (~930 likes / ~56k views) as a design reference.
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jev-mcp open-sources nine TypeSafe Jev judgment tools for Cursor and Codex

@sora19ai highlighted burnigtm/jev-mcp, a local stdio MCP that exposes TypeSafe Jev structured judgments to Cursor, Codex, and other MCP clients while the host still edits files and runs commands. Nine tools include jev_step, jev_review, jev_verify, and jev_rank for patch review, claim checks, and ranking; a TypeSafe API key is required, with mock mode for demos. MIT-licensed with about 13 GitHub stars at verification.

Key Takeaways

  • Local MCP that puts Jev judgments on Cursor/Codex loops while the host still executes tools.
  • Nine tools cover step routing, patch review, claim verification, screening, and ranking.
  • Needs a TypeSafe API key; burnigtm/jev-mcp under MIT with about 13 stars at verification.
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