AI Coding Live News — Real-Time 𝕏/Twitter Updates & Breakthroughs

Aleph Alpha@AlephAlpha·Collected 31d ago

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Aleph Alpha open-sources Kolibri-1: 78B MoE (3.46B active), bilingual DE/EN Apache-2.0; SWE-Verified 66.4

On 3 Oct 2026, Aleph Alpha released open-weight Kolibri-1 (HF, Apache-2.0): 78B MoE / ~3.46B active, bilingual DE/EN, 262k native context (1M extrapolated), reasoning effort + tools. Official card: LiveCodeBench v6 85.9, SWE-Bench Verified 66.4, TerminalBench 2.1 27.7. Serve via aleph-alpha-inference + vLLM.

Key Takeaways

Shipped 2026-10-03; weights Aleph-Alpha/Kolibri-1 Apache-2.0; blog on aleph-alpha.com
78B total / ~3.46B active; ~78GB FP8; native 262k context (validated to 1M)
Official card: LiveCodeBench v6 85.9 · SWE-Verified 66.4 · TerminalBench 2.1 27.7 · HumanEval+ 92.7
Serve: aleph-alpha-inference + vllm serve Aleph-Alpha/Kolibri-1 with kolibri1 parsers
Sovereign DE/EN on-prem focus — not a Terminal-Bench leader vs denser peers

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Linked Models & Agents:

⚡ Kolibri-1⚡ Kolibri⚡ Kolibri Origin🌐 Aleph Alpha

#Kolibri#Aleph alpha#Open weight#Moe#Foundation model#German#apache-2.0#Vllm#Coding#Sovereign AI#Long context

Cline@cline·Collected 38d ago

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Cline Desktop v0.0.37: About & What's new surfaces; Bedrock OpenAI inference profiles fix reasoningConfig errors

Cline Desktop v0.0.37 (2026-09-26) adds Settings → About (version/channel, check/restart updates, recent release notes, report issue), a one-time What's new dialog after updates, macOS Help→Export Diagnostics, and fixes Bedrock OpenAI inference-profile calls that failed with Unknown parameter reasoningConfig when reasoning effort was set.

Key Takeaways

Shipped: desktop-v0.0.37 vs desktop-v0.0.36 on GitHub Releases
UX: Settings About page with version/channel, update buttons, release notes links, Report an issue
Onboarding: one-time What's new after update; Highlights on About reopens it
Ops: macOS Help→Export Diagnostics opens the export directly
Bedrock: OpenAI models via us.openai./global.openai. inference profiles no longer fail on reasoningConfig when reasoning effort is set

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Linked Models & Agents:

⚡ GPT-6🤖 Cline (VS Code Agent)🌐 Cline 🌐 Amazon 🌐 OpenAI

#Cline Desktop#Bedrock#inference profile#reasoningConfig#What's new#Diagnostics#coding agent

vechen@miu21590·Collected 43d ago

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Mid-task Jev in Codex raises/lowers GPT-6 Astra reasoning effort; author reports ~50% Astra cost cut without breaking prompt cache

@miu21590 demoed using Jev inside a running Codex session to raise/lower GPT-6 Astra reasoning effort—more thinking when stuck, less on routine steps—reporting ~50% lower Astra costs and faster runs without breaking prompt caching. Versus the usual pre-task Jev model pick, this wires the decision model mid-run for cost control.

Key Takeaways

Call Jev mid-Codex-run to raise/lower GPT-6 Astra reasoning effort.
Author tests: ~50% lower Astra cost, faster, prompt cache intact.
Moves Jev from pre-task model pick to in-task cost control.

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Linked Models & Agents:

🤖 codex 🌐 OpenAI

#Codex#Jev#GPT-6#Astra#reasoning effort#prompt cache#Cost

Artificial Analysis@ArtificialAnlys·Collected 57d ago

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Artificial Analysis Intelligence Index v4.3: Terminal-Bench 4.0 + AutomationBench-AA

Artificial Analysis (@ArtificialAnlys) shipped Intelligence Index v4.3: Terminal-Bench upgraded 2.1→4.0 (now mini-SWE-agent harness) and τ³-Banking replaced by AutomationBench-AA (657 private Zapier business workflows). Claude Fable 5.1 and GPT-6 Astra tie at 53; OpenAI holds most of the intelligence-vs-cost frontier across Astra reasoning efforts.

Key Takeaways

Benchmarks: Index v4.3 upgrades Terminal-Bench to 4.0 and adds AutomationBench-AA (private set).
Weighting: private-task/answer evals rise from 40% to 45%; category weights unchanged.
Results: Claude Fable 5.1 and GPT-6 Astra tie at 53; open-weights led by GLM-5.3 / Kimi K3 at 44.

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Linked Models & Agents:

⚡ GPT-6 Astra ⚡ Claude Fable 5.1 🌐 OpenAI 🌐 Anthropic

#Artificial Analysis#Intelligence Index#Terminal-Bench#AutomationBench-AA#GPT-6 Astra#Claude Fable 5.1#Benchmark

melvin@melvindvivas·Collected 57d ago

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JSONL Viewer v1.3.4: nest Codex subagent traces under parent runs with MAIN/SUBAGENT nav

melvin (@melvindvivas) shipped JSONL Viewer v1.3.4: Codex subagent traces now nest under the parent run that spawned them, with MAIN/SUBAGENT labels, parent-child navigation, and per-turn model plus reasoning effort.

Key Takeaways

Release: JSONL Viewer v1.3.4 (github.com/donvito/jsonl-viewer).
Traces: Codex subagents nest under parent runs with MAIN/SUBAGENT labels.
Nav: parent-child jumps; per-turn model and reasoning effort.

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Linked Models & Agents:

🤖 openai-codex

#JSONL Viewer#Codex#Subagent#Traces#v1.3.4#Debugging

Tibo@thsottiaux·Collected 58d ago

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OpenAI Codex lead: GPT-6 Astra on low beats GPT-5.6 Sol on high — dial effort down

OpenAI Codex lead Tibo (@thsottiaux) calibrates Astra reasoning effort: GPT-6 Astra on low already outperforms GPT-5.6 Sol on high. If you were happy with Sol on high, move Astra down to low or medium to avoid over-spending effort.

Key Takeaways

Calibration: Astra low already beats Sol high.
Guidance: former Sol-high users should try Astra low/medium.
Impact: lower default effort can hit quality with less usage burn.

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Linked Models & Agents:

⚡ GPT-6 Astra ⚡ GPT-5.6 Sol 🤖 openai-codex 🌐 OpenAI

#GPT-6 Astra#GPT-5.6 Sol#Codex#reasoning effort#OpenAI#Tibo

GHCopilotLog@GHCopilotLog·Collected 59d ago

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GitHub Copilot App weekly: /goal for local autopilot, MCP deep-link install, Power BI MCP

GitHub Copilot App weekly recap (Aug 29–Sep 5): 1 release and 237 improvements. Highlights include /goal for persistent autopilot objectives in local sessions, MCP install via deep link with a prefilled review form, Power BI MCP in the catalog (hosted remote and local), and configurable reasoning effort for code-review agents. Notes: github.com/github/app/releases.

Key Takeaways

/goal sets persistent objectives for local autopilot sessions.
Install MCP servers via deep link with a prefilled confirmation form.
Power BI MCP joins the catalog; code-review agents can set reasoning effort.

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Linked Models & Agents:

🤖 GitHub Copilot (Agent Mode / Codex)🌐 github 🌐 Microsoft Azure AI

#GitHub Copilot App#/goal#MCP#Power BI MCP#Autopilot#code review

Nikunj Handa@nikunjhanda·Collected 60d ago

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OpenAI: GPT-6 Astra Responses API adds async function calling and mid-turn steering

OpenAI engineer Nikunj Handa says GPT-6 Astra ships three Responses API upgrades: async function calling (the model can keep going while tools run), mid-turn steering (inject messages or tool outputs during reasoning), and changing reasoning effort without breaking cache. Details are in OpenAI’s latest-model developer guide.

Key Takeaways

Async function calling: model continues while tools run, cutting idle multi-tool loops.
Mid-turn steering: inject messages or tool outputs during reasoning without a full restart.
Change reasoning effort without breaking cache—useful for cost/latency tuning.

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Linked Models & Agents:

🌐 OpenAI

#GPT-6 Astra#Responses API#async function calling#mid-turn steering#reasoning effort#OpenAI#API

OpenAI@OpenAI·Collected 65d ago

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OpenAI Announces o3-mini Full API Availability: Custom Reasoning Budgets & 200k Context

OpenAI made o3-mini reasoning API generally available with flexible reasoning effort tiers (low/medium/high) at $1.10/1M input.

Key Takeaways

Reaches 71.2% on SWE-bench Verified, rivaling human grandmaster algorithm performance
Dynamic reasoning_effort parameter balances millisecond completion with deep architectural synthesis
Full Prompt Caching support dropping cached token cost to $0.55/1M

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Linked Models & Agents:

⚡ OpenAI o3-mini (High Reasoning Coding)⚡ GPT-5.6 Sol 🤖 Devin 2.0 (Cognition)🤖 Cursor Composer (Agent Mode)🤖 Cline (VS Code Agent)🌐 OpenAI

#OpenAI#O3 mini#SWE-bench 71.2%#Deep Reasoning#Prompt Caching

DeepSeek@deepseek_ai·Collected 65d ago

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DeepSeek-V4-Pro officially launches with variable reasoning effort and native Codex support

DeepSeek officially launched the production-ready V4-Pro model. Featuring enhanced agentic stability in production, it supports low, high, and max reasoning effort toggles, native OpenAI Responses API compatibility, and one-click Codex integration alongside a 50% off-peak pricing tier.

Key Takeaways

Optimized for production agent loop stability and precise multi-tool execution.
Configurable reasoning effort (low, high, max) balances latency with algorithmic depth.
Native OpenAI Responses API support paired with a 50% off-peak pricing schedule.

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Linked Models & Agents:

⚡ DeepSeek-V4 (Next-Gen MoE)⚡ DeepSeek-R1 (Open Reasoning)🌐 DeepSeek

#Deepseek#Deepseek v4 pro#Codex#API release#Off peak pricing

Paul Gauthier@paulgauthier·Collected 65d ago

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Aider Polyglot benchmark: OpenAI o3-pro sets new 85% SOTA for multilingual code editing

Aider creator Paul Gauthier announced that OpenAI o3-pro with high reasoning effort set a new record of 85% on the Aider Polyglot coding benchmark. The benchmark evaluates multi-language real-world codebase editing, solidifying o3-pro's position at the top of practical coding leaderboards.

Key Takeaways

o3-pro achieves an 85% solve rate on Aider Polyglot with high reasoning effort, setting a new SOTA.
Aider real-repo benchmark remains a gold-standard yardstick for code editing accuracy.
Demonstrates an 18%+ leap in unified multi-language refactoring over previous generation models.

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Linked Models & Agents:

⚡ OpenAI o3-mini (High Reasoning Coding)🤖 Aider 🌐 OpenAI

#Aider#O3 pro#Polyglot benchmark#Sota#Code editing#Leaderboard