AI Coding Live News

Vercel Security Review Enters Public Beta: Vulnerability Review on Every GitHub Push, One-Click Fixes with Vercel Agent

On November 3 (US Pacific time), Vercel launched Security Review in public beta. It is a new Vercel Agent capability alongside Code Review and Investigation: once enabled, every push to connected GitHub repositories triggers a diff-only vulnerability review that finishes in seconds. Findings land in the Security dashboard, PR comments and the CLI, and can be fixed with one click by Vercel Agent or pulled into your own coding agent via vercel security vulnerabilities. It is available to Pro and Enterprise teams, and reviews are free until November 10.

Vercel Security Review Enters Public Beta: Vulnerability Review on Every GitHub Push, One-Click Fixes with Vercel Agent

Key Takeaways

  • Trigger: every GitHub push (including direct pushes to production) is reviewed automatically; only the diff is scanned, which Vercel says finishes in seconds, so it can run on every preview and production deployment
  • Beyond pattern matching: the agent traces how changed code is reached, checks existing protections and rechecks each finding, which Vercel says catches logic flaws such as missing authorization checks
  • Teams can write security policies in plain language, scoped to projects or the whole team, and manage them with vercel security policy
  • Pricing: Vercel Agent rate, model tokens at provider cost with no markup, plus $0.25 per million tokens; reviews are free until November 10, 2026, while Agent fixes are billed
  • Availability: public beta for Pro and Enterprise teams; Vercel has not published detection-rate or false-positive benchmarks
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Pruna Open-Sources P-H3-Image: MiniMax-H3 Video Model Turned Into an Image Generation and Editing Model, 8 Steps, 5x Faster, 7x Smaller

On November 3, model-compression company Pruna AI open-sourced P-H3-Image, which turns MiniMax-H3, the open-weights video model MiniMax released this summer, into a text-to-image and image-editing model. Per Pruna: sampling drops from 50 to 8 steps, a 4 MP image on one B200 goes from 12.4 s to 2.6 s, the 4-bit set is a 20 GB download (vs 143 GB for H3), and peak VRAM at 8 MP is about 21 GB. It renders 2 to 16 MP and edits with multiple reference images. Weights are on Hugging Face with native ComfyUI support. It keeps the MiniMax H3 Community License, which by default excludes the EU, UK, South Korea and the US.

Pruna Open-Sources P-H3-Image: MiniMax-H3 Video Model Turned Into an Image Generation and Editing Model, 8 Steps, 5x Faster, 7x Smaller

Key Takeaways

  • Speed (Pruna figures): 8 steps instead of 50; on one B200 a 4 MP image goes from 12.4 s to 2.6 s and 8 MP from 30.6 s to 5.8 s; the 4-bit set does 4 MP in about 6.3 s on an L40S
  • Size: the transformer shrinks from 33B to 20B parameters and the text encoder is distilled from 32B to 4B (67 GB to 9 GB); downloads are 55 GB in bf16 and 20 GB in 4-bit vs 143 GB for H3
  • Memory: peak VRAM at 8 MP is about 56 GB in bf16 and 21 GB in 4-bit; the bf16 diffusers pipeline with the prompt upsampler needs about 100 GB
  • Capabilities: renders 2 to 16 MP, strongest at photorealistic and documentary photography, film stills, anime and graphics with short text; supports multi-reference editing (8 images per the X post, 9 per the blog)
  • License: MiniMax H3 Community License; the EU, UK, South Korea and US need a separate license from MiniMax, and products must show "Powered by MiniMax H3"
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Linked Models & Agents:
⚡ P-H3-Image⚡ MiniMax-H3⚡ Qwen3.5-9B🌐 Pruna AI🌐 MiniMax🌐 Hugging Face🌐 ComfyUI

X Discussion: Paul Graham Pushes Back on an "AI Bubble" Post, Saying AI Cannot Be Both Fake and Destroying Jobs

On November 2, X user Fav (@favwontmiss) posted that "the AI bubble bursting is going to be one of the biggest reality checks in tech history," accusing AI companies of burning billions, destroying jobs, draining natural resources and forcing products nobody asked for into daily life. The post drew about 6,600 likes, 670 reposts and 370,000 views. On November 3, Y Combinator co-founder Paul Graham (@paulg) replied: "You can't claim both that AI is fake and that it's destroying jobs. If it were fake it wouldn't be able to." His reply drew about 1,600 likes and 44,000 views, and the debate reached X trending. These are opinions from both sides, not established facts.

Key Takeaways

  • The original post: Fav (@favwontmiss) called an AI bubble burst "one of the biggest reality checks in tech history," with about 6,600 likes, 670 reposts and 370,000 views; see the original post.
  • Paul Graham's rebuttal: the critiques "AI is fake" and "AI is destroying jobs" contradict each other; see his reply, with about 1,600 likes and 44,000 views.
  • Where the sides talk past each other: "bubble" is about valuations and whether spending pays back, while "replacing jobs" is about whether the capability is real. Both can hold at once (useful technology that is still overvalued), which is a recurring point in the replies.
  • Why it matters to developers: the argument does not change what the tools can do, but it shapes enterprise AI budgets, hiring narratives and public acceptance of AI coding products.
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CrowdStrike Agrees to Acquire Fiddler and Dreadnode and Ships an AI Kill Switch, Bringing Agent Observability, Pre-Deployment AI Security Testing and Agent Termination Into Falcon

At Fal.Con Europe in Barcelona on November 3, CrowdStrike said it signed definitive agreements to acquire AI agent observability company Fiddler AI and automated AI security testing company Dreadnode, and launched an AI Kill Switch in Falcon Guardian that can terminate a misbehaving agent, block it from relaunching and cut its enterprise network access. Both deals are expected to close in CrowdStrike's fiscal Q4 FY27, with the products generally available that quarter. CrowdStrike did not disclose prices; CyberScoop, citing the Wall Street Journal, put Fiddler at about $400 million and Dreadnode at about $95 million.

CrowdStrike Agrees to Acquire Fiddler and Dreadnode and Ships an AI Kill Switch, Bringing Agent Observability, Pre-Deployment AI Security Testing and Agent Termination Into Falcon

Key Takeaways

  • Both acquisitions were announced the same day and are expected to close in CrowdStrike's fiscal Q4 FY27, paid mostly in cash plus some stock subject to vesting conditions.
  • CyberScoop, citing the Wall Street Journal, puts Fiddler at about $400 million and Dreadnode at about $95 million, roughly $495 million combined; CrowdStrike did not disclose the amounts.
  • Fiddler will be sold as Falcon Observe: pre-deployment evaluation, tracing of decisions and tool calls across the agent hierarchy, drift and hallucination detection, and evaluation costs cut by up to 98% using purpose-built models.
  • Dreadnode runs more than 100 known AI attack strategies (jailbreaks, prompt injection, multi-turn and agent-specific attacks) against models, agent reasoning loops, applications and MCP servers, mapping findings to OWASP LLM Top 10, OWASP Agentic ASI01-ASI10, MITRE ATLAS and NIST AI RMF.
  • The AI Kill Switch is available today: an authorized admin triggers it, the agent is stopped first at the Gateway (endpoint enforcement coming soon), blocked from relaunching and cut off from the network, with every action logged.
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Anthropic Open-Sources FlashProve: zkSNARK Proofs That a Reply Came From the Committed Model, About 110 Tokens/s for 400B Llama 4 Maverick on One H100

Anthropic's Frontier Red Team published a research post on November 2 and open-sourced FlashProve under Apache 2.0. A model provider commits to hidden weights once, then attaches a zero-knowledge proof to each reply, so a user without the weights can check on an ordinary CPU that the reply really came from that model. That targets routing services that quietly swap in cheaper models and tampered token streams. Anthropic reports about 110 tokens/s on one H100 for Llama 4 Maverick (400B MoE, 4-bit), proofs of about 19 MB per 1,024-token request, and roughly 70-95x the GPU time of vLLM inference. It is an unmaintained research prototype limited to 1,024-token contexts and greedy decoding of an integer version of the model. Anthropic engineer Steve Weis shared the release on X.

Anthropic Open-Sources FlashProve: zkSNARK Proofs That a Reply Came From the Committed Model, About 110 Tokens/s for 400B Llama 4 Maverick on One H100

Key Takeaways

  • Anthropic-reported: on Llama 4 Maverick (400B MoE, 4-bit) one H100 proves about 110 tokens/s (prefill, 24 requests per batch), with proofs of about 19 MB per 1,024-token request.
  • It is costly: roughly 70-95x the GPU time of vLLM inference; the performance page lists about 270 GB of host memory for serving and about 39 minutes to commit to a set of weights.
  • Verification needs no GPU: the C++ verifier checks a proof in about 21 seconds on one thread, and a slower Lean verifier plus Lean 4 proofs of the security theorem are included.
  • Against prior work: VerInf proved the same model at about 0.02 tokens/s; the commercial Attestable reached 53-77 tokens/s on Gemma 4 31B but has no open-source release.
  • Limits: v0.16.0 is an unmaintained research prototype with 1,024-token contexts, greedy decoding of an integer model only, and no architecture hiding.
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DeepSeek Harness Launches Creator Mode: Describe a Plugin in Chat and the Agent Builds It, With 7 Official Demo Prompts

DeepSeek Harness, DeepSeek's open-source, MIT-licensed agent app, announced Creator Mode on its official X account on November 2: users describe the plugin they want in plain language, and the agent turns it into a plugin they can use. Demos include a 3D landmark gallery, a 3D Transformer visualizer, a video editing workbench and a paper reader. DeepSeek also published seven bilingual demo prompts on cdn.deepseek.com, cautioning that some plugins took several rounds of conversation, so a single prompt may not reproduce the video exactly. The post drew about 20,000 likes and 1.69 million views. No benchmarks were published.

DeepSeek Harness Launches Creator Mode: Describe a Plugin in Chat and the Agent Builds It, With 7 Official Demo Prompts

Key Takeaways

  • Creator Mode: describe the plugin you want in natural language and the agent turns it into a usable plugin, from a design workspace to a sandbox game or a 3D LLM visualizer.
  • DeepSeek published 7 demo prompts: a 3D landmark gallery, a 3D Transformer demo, a five-style app theme switcher, a poster workbench, a video editor, a paper reader and a token-usage statistics plugin.
  • The entry point shipped in v0.2.1-rc.5 (Oct 30 pre-release): plugin management gained a "Let Agent create a plugin" button that opens Creator mode and keeps the draft.
  • The GitHub repo has about 270,000 stars under the MIT license; Harness is still in preview.
  • No benchmarks or success rates were published; the announcement drew about 20,000 likes and 1.69 million views.
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Deep Principle Open-Sources Hydrogen-1.0, a 753B Science Model Post-Trained From GLM-5.3 on Lab Records, MIT Licensed

Hangzhou-based AI-for-science company Deep Principle released Hydrogen-1.0 on November 2, its first general-purpose scientific model. It post-trains Zhipu's GLM-5.3 with SFT and RL on chemistry, materials and life-science data plus its own lab records and discovery trajectories, and ships about 753B parameters of BF16 weights on Hugging Face under the MIT license. By the vendor's own evaluation it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra; none of the results have been independently reproduced yet.

Deep Principle Open-Sources Hydrogen-1.0, a 753B Science Model Post-Trained From GLM-5.3 on Lab Records, MIT Licensed

Key Takeaways

  • Scale: Hugging Face lists about 753B total parameters (BF16, roughly 1.5 TB of weights), glm_moe_dsa architecture, 1M-token context and 128K max output, MIT licensed (download requires accepting HF terms).
  • Benchmarks (vendor-reported): 38.3% on FrontierScience-Research vs 33.3% for GPT-6 Astra; 93.0% on GPQA Diamond, which the company says roughly matches Kimi K3.
  • Internal scenarios (vendor-reported): leads max-thinking GPT-6 Astra by 5.13 points on scientific computing and 4.76 points on forward reaction prediction, but trails it by 3.33 points on NMR structure elucidation.
  • Materials discovery: after five rounds of transition-metal-complex search, the mean best HOMO-LUMO gap reached 4.318 eV vs 3.689 eV for GPT-6 Astra and 3.353 eV for Kimi K3 (official blog).
  • Access: weights on HF and ModelScope, plus NVFP4 (B200/B300) and MXFP4 (MI350X/MI355X) quantized repos; API via NeuroSpark and in-app use in the Mira AI Scientist; tested with SGLang on 8 GPUs.
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Microsoft Open-Sources Mage-VL-Cowork, a 4B Computer-Use Agent That Chooses Between Clicking the GUI and Running Bash, Scoring 66.0% on OSWorld-Verified

On October 30, Microsoft's Mage team released Mage-VL-Cowork on Hugging Face under Apache-2.0: a 4B multimodal computer-use agent in which a single model decides at every step whether to act through screenshots and GUI clicks (computer_use) or through bash and Python in a terminal, with no external orchestrator. Microsoft reports a two-run average of 66.0% on OSWorld-Verified (361 tasks, 50-step budget), above Qwen3.6-27B at 63.9% but below the 9B SCALECUA-Qwen3.5-9B at 68.7%.

Microsoft Open-Sources Mage-VL-Cowork, a 4B Computer-Use Agent That Chooses Between Clicking the GUI and Running Bash, Scoring 66.0% on OSWorld-Verified

Key Takeaways

  • 4B parameters, built on Mage-VL (Mage-ViT vision encoder plus Qwen3-4B-Instruct-2507); about 9.48 GB of bf16 weights under Apache-2.0.
  • On OSWorld-Verified, two independent runs scored 65.33% and 66.75% for a 66.0% average; 87.5% on operating-system tasks and 62.7% on multi-app workflows.
  • In Microsoft's side-by-side, a LibreOffice Calc chart task took 39 seconds (6 steps) via Python in the terminal, versus 9:56 (39 steps) for GUI-only Qwen3.6-27B.
  • Online RL adds 10.16 points over hybrid SFT while cutting mean model calls by 13.0% and output tokens by 25.4%.
  • Training data came from Mage-CUA-Forge, which synthesized about 294K desktop and web tasks across 6,915 skills and cut per-task generation cost from $0.906 to $0.158.
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Linked Models & Agents:
⚡ Mage-VL-Cowork⚡ Mage-VL⚡ Qwen3-4B⚡ Qwen3.6-27B🌐 Microsoft

X Discussion: Vercel CEO Guillermo Rauch Says "Own Your Intelligence," Urging Local Models to Keep Spreading and Developers to Own Their Agent Harness and Data

Vercel CEO Guillermo Rauch posted on X on November 1: "Own your intelligence. Anyone that opposes this has something to sell you (perhaps your own intelligence back to you.)" He argued that local models need to keep proliferating; since the biggest models may be out of reach locally for now, the best option is to keep your options open: own your harness, data, connections and runtime, meaning your agents, and embrace open source. Within about 12 hours the post drew about 2,500 likes, 128K views and over 100 quote posts, with debate centered on how to balance local and cloud AI.

Key Takeaways

  • This is Rauch's personal opinion post, not an official Vercel announcement or product release.
  • The original post (2026-11-01 21:08 UTC) had about 2,533 likes, 255 reposts, 266 replies, 107 quotes, 128K views and 581 bookmarks at fetch time.
  • Core claim: local models should keep spreading; until the biggest models can run locally, keep your options open by owning your harness, data, connections, runtime and agents, and embrace open source.
  • Mark (@marksuman), co-founder and CEO of Maple AI, replied in a quote post that it will be hybrid: cloud will always be more powerful, so local-first AI should be paired with private cloud.
  • Engineer Shantanu Goel (@shantanugoel) quoted it saying "Guillermo says the truth that most others won't!", drawing about 37 likes.
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X discussion: open-source AI builder Jun Song says he turned down an anonymous black hat group offering huge money to post-train open-weight models on exploit data

At 02:20 UTC on Nov 1, open-source AI builder Jun Song (@jun_song, who builds Supergemma) posted on X that an anonymous black hat group offered him an "astronomical" sum to post-train open-weight models with reinforcement learning on the exploit data it holds. He said the volume was so large he would have believed it was an undercover FBI dataset, that he turned it down, and that what scares him is that someone else will eventually take the deal. As of 17:10 SGT the post had about 2,029 likes, 222K views and 771 bookmarks, and it has been rewritten as a parody template. This is a personal account; the offer, the data volume and the group cannot be verified, so it is treated here as a discussion.

Key Takeaways

  • Reach: the original post had about 2,029 likes, 127 reposts, 101 replies, 771 bookmarks and 222.2K views as of Nov 1, 17:10 SGT
  • Claim: an anonymous black hat group offered an "astronomical" sum to post-train open-weight models on exploit data; he says he declined, and neither the amount nor the data volume is public or verifiable
  • Comparison: he says a white-hat project of his built on GLM-5.3 used under 1% of that volume and already beat frontier models "by a huge margin"; this is self-reported with no public benchmark
  • Reaction: accounts such as msuiche and Samuel Cardillo reused the opening lines as a parody template, so the thread splits between taking it seriously and joking about it
  • Issue: open-weight models cannot be recalled once released and their safeguards can be trained away, so a request like this makes the open-weights versus misuse debate concrete
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guregodevo (Memdoor author)github.com·
👀21
🔥81

Memdoor reproduces GVS5H: a team of five Qwen3.8-27B agents averages 9.8/10 on the first 10 LiveCodeBench Hard problems, against 8.0 for a single Fable 5 call

On Oct 31 the open-source coding agent Memdoor (GitHub guregodevo/memdoor, Apache 2.0) published its team-solve workflow: using the GVS5H paper authors' exact prompts and loop, five Qwen3.8-27B instances act as planner, two ideators, solver and manager, talk only through files on disk, and a script that runs the tests decides success. Over five passes on the first 10 LiveCodeBench Hard problems from the GVS5H list it averaged 9.8/10, against 9.6 for the authors' own harness and 8.0 for a single Fable 5 call; 49 of 50 runs solved the problem for $35.79 in total, and every request and reply is public. The Hacker News post has about 200 points. This is a community reproduction on only 10 problems, and the author notes that training-data contamination cannot be ruled out.

Key Takeaways

  • Score: a mean of 9.8/10 over five passes (10, 10, 10, 10, 9), against 9.6 for the GVS5H authors' own harness and 8.0 for a single Fable 5 call (team-solve README, project-reported)
  • Cost: 49 of 50 runs solved, $35.79 and 15.3M output tokens in total; a median run took about 40 minutes of model time and $0.39, the longest 4 hours
  • Method: five roles (planner, two ideators, solver, manager) plus a repair worker each start from a fresh context, talk only through files on disk, and a shell script that runs the tests decides success instead of a model
  • Limits: only 10 problems rather than the paper's 100; the contests date from March and April 2025 and may be in training data; Fable 5's column comes from the GVS5H authors' result files and was not rerun in this harness
  • Reach: the HN post has about 200 points as of Nov 1, 16:10 SGT; the original GVS5H paper is arXiv 2608.26480, which reached #1 on HN on Oct 25
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SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work

On Oct 30, SenseTime released SenseNova-Loop-9B-Preview (Apache 2.0) on its official X account and Hugging Face. It runs layers 4 to 27 of a 32-layer decoder twice with the same weights, so each forward pass executes 56 layer computations while storing about 9B parameters, about 14.6B at inference, and it uses two-stream mHC (manifold-constrained hyper-connections) to manage the residual streams. It takes text and images, with training sequences up to 262,144 tokens. SenseTime charts show it beating Qwen3.5 and Qwen3.6 peers on BIRD-Dev, WorkBuddy Bench, and other agent and data tasks, while it still trails on Terminal Bench 2.1; the technical report is not out yet. The official launch post drew about 910 likes.

SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work

Key Takeaways

  • Structure: 32 distinct decoder layers, with layers 4 to 27 looped twice on shared weights for 56 layer computations per forward pass; about 9B stored and about 14.6B inference parameters (model card)
  • Vendor-reported scores: BIRD-Dev 71.38 (Qwen3.5-27B 69.84), WorkBuddy Bench 66.89 (Qwen3.6-35B-A3B 61.98), AutomationBench 13.72 (Qwen3.5-27B 10.17)
  • Weak spots from the same vendor chart: Terminal Bench 2.1 at 40.45, below Qwen3.6-27B at 47.19; tau3-Banking at 18.56, slightly below Qwen3.5-27B at 19.59
  • Training context up to 262,144 tokens; hybrid attention uses three linear-attention layers plus one full-attention layer per four-layer group; MTP speedup is available, DSpark and DFlash2 are still training
  • Apache 2.0, requires trust_remote_code=True; official examples cover Transformers 5.18.0, a 4-GPU LightLLM setup, and an SGLang PR, and the technical report is still to come
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Linked Models & Agents:
⚡ SenseNova-Loop-9B-Preview⚡ Qwen3.5-27B⚡ Qwen3.6-35B-A3B🌐 SenseTime

Benji Taylor, head of design at X and SpaceXSI, open-sources Stageshow, an agent skill for realistic faux product UI and animated demos

On Oct 31, Benji Taylor, head of design at X and SpaceXSI, released Stageshow on X: an open-source skill (MIT license) for coding agents that helps them produce faux product UI that looks real, including stills, cursor demo animations, and bento grids, delivered as ordinary React components. It installs with one command, and he says it works with Grok Build, Claude Code, Codex, Gemini, and others. The launch post drew about 4,500 likes and 6,500 bookmarks. He notes it rarely nails a demo in one shot and most still take a few rounds of feedback.

Benji Taylor, head of design at X and SpaceXSI, open-sources Stageshow, an agent skill for realistic faux product UI and animated demos

Key Takeaways

  • Install: npx skills add benjitaylor/skills --skill stageshow; the author says it works with Grok Build, Claude Code, Codex, Gemini, and other agents (author's post)
  • Contents: a seven-step workflow in SKILL.md, reference rule docs, dozens of gold examples, a React kit, and self-check scripts such as film.mjs (GitHub)
  • License: the stageshow folder is MIT licensed; the repo was created Oct 26 and had about 61 stars at writing time
  • Reach: the launch post had about 4,521 likes, 6,561 bookmarks, and 213k views as of Nov 1, 08:00 SGT
  • Quality claims are the author's own: the same prompts did better with the skill, but no quantitative evaluation was published
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DeepSeek Harness team open-sources dsh-superagent, a proactive coding agent prototype for a single GitHub repo, alongside its Super-agent architecture article

On Oct 31, deepseek-ai open-sourced dsh-superagent-prototype on GitHub under the MIT license: a proactive coding agent that runs as a DeepSeek Harness bundle, lives on one GitHub repository and can optionally listen to Feishu. When an allowed person asks for work, it opens a work item, works in its own session and directory, and answers in the original conversation, for example with a pull request. The README states it is a research prototype, not an official DeepSeek product. The companion article, Exploring Super-agent Design, proposes a two-layer Router-Worker architecture; the team test, as reproduced by media, reports a p95 completion time of 15.7 minutes versus 29.5 minutes for a single-session setup.

Key Takeaways

  • Open source: deepseek-ai/dsh-superagent-prototype went public on Oct 31 under MIT, written in TypeScript, with about 1,800 stars at the time of writing
  • Scope: each deployment serves 1 GitHub repository, with optional Feishu; it acts only when an allowed person addresses it or a team-written rule allows it
  • Delivery: by default it only allows auto-merge for an approved pull request opened for a ticket with fewer than 200 changed lines, and never enables auto-merge as the agent account
  • Team test (as reproduced by media, vendor-reported): in a simulation with about 300 messages and 20 requests, p50 / p95 completion was 12.4 / 15.7 minutes versus 20.4 / 29.5 for a single session with a subagent tool
  • Risk: worker sessions run commands without approval as the operator's OS user, and the README asks users to review and adapt the code first
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OpenCode team ships Hex 3.0 stable: local voice dictation for Mac, Windows and Linux, built for talking to coding agents

Anomaly, the company behind open-source coding agent OpenCode, shipped Hex 3.0 stable: hold a shortcut, speak, and the text is pasted at your cursor. Recognition runs on-device, local model memory drops from about 900 MB to about 160 MB, and it adds vocabulary hints, a hex CLI and optional OpenCode rewriting.

OpenCode team ships Hex 3.0 stable: local voice dictation for Mac, Windows and Linux, built for talking to coding agents

Key Takeaways

  • Hex v3.0.0 was released on GitHub at 00:38 UTC on Oct 31; it is MIT-licensed, written in Rust, and the repo has about 1,557 stars (release notes)
  • Vendor figures: local model memory drops from about 900 MB to about 160 MB; since 2.3.0 it streams, so a 2-minute dictation finishes about 0.1 s after release on an RTX 5070 Ti
  • Runs on macOS 15+ (Apple silicon), Windows x64/ARM64 and Linux x64/ARM64, with new .deb and .rpm packages on Linux
  • 3.0 does not auto-update from 2.3.1 and starts fresh, so settings, History and models must be set up again; Voice Action and spoken commands are removed
  • The @opencode launch post drew about 2,400 likes, 1,600 bookmarks and 100k+ views (post)
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Linked Models & Agents:
⚡ Parakeet⚡ Whisper🤖 OpenCode🤖 Hex🌐 Anomaly