AI Coding Live News

Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

On Nov 2 Meituan's LongCat team listed LongCat-2.5 in its API platform change log as the full release of September 25's LongCat-2.5-Preview. The official technical blog says it has 1.6 trillion total parameters with about 48 billion activated per token, builds on LongCat-2.0 with stronger native visual understanding and agentic coding, and was trained and served with W4A4 inference on domestic AI accelerators. Vendor-reported scores include 88.4 on Terminal Bench v2.1, 38.5 on Terminal Bench v4.0, 91.9 on BrowseComp and 83.1 on MCP Atlas. The API speaks both OpenAI and Anthropic formats with a 1M-token context and up to 128K output tokens, at a limited-time price of $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output.

Meituan ships LongCat-2.5, a 1.6T-parameter (about 48B active) native multimodal agentic model, with API pricing of $0.30/$1.20 per 1M tokens

Key Takeaways

  • Scale: a 1.6-trillion-parameter MoE with about 48B active per token, a 1M-token context and up to 128K output tokens (official blog, API docs)
  • Code agents (vendor-reported): Terminal Bench v2.1 88.4 (Claude Opus 5 89.1, GPT-5.6-Sol 88.8), Terminal Bench v4.0 38.5 (GPT-5.6-Sol 37.3, Claude Opus 5 53.9), DeepSWE v1.1 64.6
  • General agents (vendor-reported): BrowseComp 91.9, MCP Atlas 83.1, AutomationBench v1.0.6 51.8, HLE with tools 63.1; AutomationBench jumps from 15.8 on LongCat-2.0
  • Pricing: limited-time $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output (pricing page)
  • Engineering: W4A4 inference raises prefill throughput 43.3% and decode throughput 42.77% over a W8A8 baseline; FSDP gives 86% higher per-device throughput than DDP in long-sequence SFT
Read full story

Karpathy builds HackerTube in about 2 hours with Claude Code and Opus 5.5: a YouTube clone of 6,454 videos recommended on Hacker News

On Oct 31 Andrej Karpathy published HackerTube (hackertube.org) on his personal blog: a YouTube-style site that only lists the 6,454 YouTube videos recommended in Hacker News comments from 2007 to 2026, grouped into 150 topics. He says Opus 5.5 in Claude Code did the work: it downloaded every HN comment, ran Sonnet over them in a Workflow to narrow things down to about 40K comments recommending a video, wrapped the result in a YouTube UI, then deployed and hardened it on a VPS, all in about 2 hours. His only manual steps were creating a YouTube API key, booting the VPS and buying the domain. His HN submission reached the front page with about 1,005 points and 308 comments.

Key Takeaways

  • Source: Karpathy's personal blog post HackerTube (Oct 31), with the site live at hackertube.org
  • Scale: from every HN comment it narrowed things down to about 40K comments recommending a YouTube video, giving 6,454 videos across 150 topics from 2007 to 2026 (author-reported)
  • Method: Opus 5.5 ran the download and overall pipeline, used Claude Code's Workflow feature to have Sonnet filter comments and drop spam in bulk, then built a YouTube-style front end and deployed and hardened it on a VPS
  • Product details: it uses the frame at the 50% mark instead of the uploaded thumbnail, offers account-free For You picks with state kept only in the local browser, supports keyboard shortcuts, and exports the site's data so you can hand it to your own agent to build a variant
  • Workflow: Karpathy says Opus 5.5 on Medium effort is his default and he no longer reads or writes code or runs terminal commands himself; the HN post has about 1,005 points and 308 comments (public HN data as of Nov 1, 14:10 SGT)
Read full story
Linked Models & Agents:
x
xAIdocs.x.ai·
👀36
🔥82

xAI will add an invisible watermark to Grok API text by default from Nov 7, covering grok-4.7 and six other models, with per-model admin opt-out

On Oct 31, xAI (signed as SpaceXSI in its docs) added an "Output Watermarking" page to its API docs: starting November 7, 2026, text generated by grok-4.7, grok-4.6, grok-4.5, grok-4.3, and the reasoning, non-reasoning, and multi-agent versions of grok-4.20 will carry an invisible watermark by default, to comply with the EU AI Act. It covers ordinary text, structured outputs, and tool call arguments on both the Responses and Chat Completions APIs, and works by slightly shaping token choice without adding any characters. Developers can read the x-watermark response header to check; team admins can opt out per model in the Console, but must confirm a legal exception applies, and some models may be locked on.

Key Takeaways

  • Timing: on by default from November 7, 2026, with no code change and no request parameter (xAI docs)
  • Scope: 7 model IDs (grok-4.7, 4.6, 4.5, 4.3, and grok-4.20 reasoning, non-reasoning, and multi-agent) on the global and regional endpoints, across both Responses and Chat Completions
  • Coverage: ordinary text, structured outputs, and tool call arguments; the response header x-watermark: true marks a watermarked output
  • Opt-out: team admins switch it off per model, effective for new requests within 5 minutes and logged in the audit log; admins must confirm a legal exception applies, and some models are locked on
  • xAI's position: no effect on quality or speed; detection is statistical with three outcomes (watermarked, not watermarked, uncertain), and the detection API is by application
Read full story
A
Anthropic (Claude Help Center)support.claude.com·
👀8
🔥82

Anthropic extends Claude text watermarking to older models including Mythos 5, Opus 4.7/4.6 and Sonnet 4.6, reportedly from November 3

Anthropics help center article "How Claude marks AI-generated content" (updated Oct 28) now lists older models released before Aug 2, 2026, including Claude Mythos 5, Opus 4.7, Opus 4.6, Opus 4.5, Sonnet 4.6 and Sonnet 4.5, as carrying text watermarks on first-party surfaces, and says Anthropic is adding watermarks to these models to meet EU AI Act Article 50 transparency duties. According to a customer email screenshot reported by outlets such as MACCUS, the legacy rollout starts Nov 3, with Amazon Bedrock possibly a few days later; Anthropics public pages do not state that date. The watermark follows Google DeepMinds SynthID-Text approach, adds no hidden characters or extra tokens, and detection is in private preview only for regulators, media, researchers and similarly obligated organizations, which has drawn complaints on X that users cannot check their own text.

Anthropic extends Claude text watermarking to older models including Mythos 5, Opus 4.7/4.6 and Sonnet 4.6, reportedly from November 3

Key Takeaways

  • Scope: in the official support table, older models such as Mythos 5, Opus 4.7/4.6/4.5 and Sonnet 4.6/4.5 are ticked only for first-party text watermarks; cloud-partner watermarks and C2PA file credentials remain limited to models launched after Aug 2 (see the help center)
  • Timing: a customer email reported by media puts the start at Nov 3, with Bedrock possibly a few days later (MACCUS); Anthropics public pages give no date
  • Cost and quality: Anthropic says the watermark adds no characters, no extra tokens and no extra cost, using a variant of SynthID-Text (see Anthropics explainer)
  • Detection: the detection API is in private preview for regulators, law enforcement, media, fact-checkers, researchers and obligated enterprises; ordinary users cannot check text yet
  • Reach: watermarks apply worldwide across Claude, the Claude Platform API, Claude Code, Cowork and Tag, because Anthropic says it has no durable way to scope them by region yet
Read full story
ADSponsored

Wiz "Black October" report: at least nine AI developer platforms breached in October, with stolen API keys feeding an LLMjacking resale market

Cloud security firm Wiz published research on October 30 saying at least nine AI developer platforms disclosed incidents this month: Fireworks AI, Andromeda, Zeabur, Lightning AI, OpenCode, Railway, Warp, CommandCode and Northflank. Attackers hunted stored environment variables, especially keys for paid AI models; Wiz traced two stolen enterprise keys to roughly 800,000 and 1.7 million Amazon Bedrock calls, which it estimates likely ran over $100k at list price in those two cases alone.

Wiz "Black October" report: at least nine AI developer platforms breached in October, with stolen API keys feeding an LLMjacking resale market

Key Takeaways

  • Scale: between October 6 and 30, at least nine AI developer platforms disclosed incidents, and Zeabur is shutting down as a result (Wiz blog)
  • Methods: Fireworks, OpenCode and Warp reported signs of an "agent swarm" attack, and OpenCode and CommandCode attributed theirs to "distillation" attempts; the common thread is hunting stored environment variables that grant AI access
  • Case A: one stolen key drove about 800k Bedrock calls over 30 hours, about 90% successful, with Opus models making up three quarters (Wiz figures)
  • Case B: a single address made about 1.7 million calls in 13 hours, over a million successful; the two cases together likely exceeded $100k at list price
  • Resale chain: Wiz identified two sites selling discounted frontier-model access and saw both test the stolen keys
Read full story

Pika releases open-weight video model Pika H3 Flex: post-trained on MiniMax H3, six-step sampling, five audio stems, API from $0.01 per second

On October 29 Pika released Pika H3 Flex, a joint audio-video generator post-trained on MiniMax H3, with weights on Hugging Face, code on GitHub, and ComfyUI and Diffusers support. Pika says it distilled sampling from 50 denoising steps to six, averages about 1.8x faster DiT denoising than Fal H3 Max at 768p, and can produce a 15-second clip with soundtrack in as little as about 7 seconds; the hosted API adds 1080p to 4K upscaling and five exportable audio stems (dialogue, score, SFX, foley, ambience). All speed and quality figures are vendor-reported.

Pika releases open-weight video model Pika H3 Flex: post-trained on MiniMax H3, six-step sampling, five audio stems, API from $0.01 per second

Key Takeaways

  • Pricing (vendor figures): text/image-to-video from $0.01 per output second, reference-to-video from $0.03/s; Pika's comparison table lists MiniMax H3 at $0.08/s and Seedance 2.5 at $0.103/s
  • Speed (vendor figures): six-step sampling, about 1.8x faster DiT denoising than Fal H3 Max at 768p on average and about 2x for 10–15 s text/image-to-video; 1.60x faster end-to-end 5 s 768p generation on eight B300 GPUs
  • Ranking: Pika's product page says it scores 1293 and ranks #7 on DesignArena's text-to-video board, the highest open-weight model there
  • Release form: the Hugging Face repo is tagged as an adapter on MiniMax-H3 under an 'other' license; local inference natively supports 480p/768p with mixed stereo audio, while 4K upscaling and audio stems are hosted-API only
  • Accepts up to 12 reference inputs (image, audio, video, sketch and more) and supports style transfer, motion transfer, character swap and targeted edits
Read full story
Linked Models & Agents:
⚡ Pika H3 Flex⚡ MiniMax H3⚡ Fal H3 Max⚡ HeyGen Video⚡ Gemini Omni 1.1 Flash🌐 Pika🌐 MiniMax

X discussion: Anthropic's head of platform engineering says a team with no Rust developers had Claude port the whole Messages API to Rust, cutting CPU by roughly 40-50%

Katelyn Lesse, Anthropic's head of platform engineering, said on the Latent Space podcast (published Oct 27) that a Rust version of the Messages API fully rolled out the previous week. The old system was a CPU-hungry Python monolith, the team started with "not a single Rust developer", humans designed and verified the system, Claude wrote the implementation, and shadow traffic compared outputs between the old and new versions. Her ballpark figures are roughly 40-50% less CPU and 10-20% better time to first token. A clip posted by Somi (@somi_ai) drew 1,000+ likes and about 140K views on X and was quoted by swyx and others, with the debate centered on whether "never do the big rewrite" is now outdated advice. These are X discussion views and spoken estimates; Anthropic has not published a written engineering report.

X discussion: Anthropic's head of platform engineering says a team with no Rust developers had Claude port the whole Messages API to Rust, cutting CPU by roughly 40-50%

Key Takeaways

  • The first-hand source is Katelyn Lesse speaking on the Latent Space episode "How Anthropic ships Claude with Claude", in the "56:05 Rebuilding the Messages API in Rust" segment
  • Lesse said the move was driven by CPU becoming a bottleneck as usage surged; CPU now has to be capacity-planned in advance, and research sandboxes, product sandboxes and CI all draw from the same pool inside Anthropic
  • The team forked the entire API in Rust in one go, ran shadow traffic to confirm the Python and Rust outputs matched, then turned it on gradually, deliberately adding no new features during the port
  • Her ballpark is about 40-50% CPU savings and 10-20% better time to first token, with larger gains on longer-running workloads; these are spoken estimates from the podcast, not a formal benchmark
  • On X, Somi called "never do the big rewrite" dead advice and swyx said Anthropic "vibecoded their entire backend to Rust"; the latter is an exaggeration, since Lesse only discussed the Messages API
Read full story
Linked Models & Agents:

OpenAI Agents API ships Auto Review, async compaction, experimental time awareness and mid-session tool updates

The OpenAI Developers account shared a post by Steve (@stevendcoffey), who works on the API at OpenAI, listing five Agents API updates this week: Codex-style Auto Review of commands before execution, compaction that runs in the background, an experimental time-awareness feature, updating a session's tools at any time, and automatic caching of system prompts across all sessions. As of writing, the official API changelog does not list these items yet.

Key Takeaways

  • Five updates: Auto Review, async compaction, time awareness (experimental), updating session tools at any time, and automatic cross-session system prompt caching
  • Auto Review mirrors the Codex mechanism and evaluates commands for potential bad behavior before they run
  • Time awareness lets the model act on time-boxed instructions such as "timebox CI fixes to 10 minutes"
  • The Agents API entered public beta on September 10, and this is another round of features within 7 weeks (after computer use on Sep 29, sandbox prewarming on Oct 9 and Ultrafast mode on Oct 16)
  • OpenAI has published no latency or cost-saving numbers, and the changelog has not listed these items yet
Read full story

OpenAI rolls out GPT-6 Rosalind Research, a GPT-6 generation life sciences model for approved research orgs in Work, Codex and the API at $15/$75 per million tokens

In its October 30 ChatGPT Business release notes, OpenAI said GPT-6 Rosalind Research is rolling out to a limited set of customers with Research approval. It shows up in the model picker in ChatGPT Work and Codex, not in the Chat tab, and separately approved organizations can call it through the API as gpt-6-rosalind. OpenAI's token-based Enterprise rate card lists it at $15 per million input tokens, $1.50 cached input and $75 output, three times the price of GPT-5.5 Rosalind Research.

Key Takeaways

  • Approved orgs only: both the organization and the user need Rosalind Research approval, and Discovery approval does not include it
  • Where to use it: the model picker in ChatGPT Work and Codex, not the Chat tab; in Codex it pairs with the Life Sciences Research plugin
  • API model name gpt-6-rosalind needs separate approval and is limited to internal research tools, not customer-facing products
  • Priced at $15 input, $1.50 cached input and $75 output per million tokens, versus $5/$0.50/$25 for GPT-5.5 Rosalind Research
  • Enterprise workspaces start with a two-week Admin Preview that is off by default until an admin enables it for approved users
Read full story
Linked Models & Agents:
⚡ GPT-6 Rosalind Research⚡ GPT-5.5 Rosalind Research⚡ GPT-6 Astra🤖 Codex🤖 ChatGPT Work🌐 OpenAI

Claude API launches the Usage and Cost Analytics API in beta and raises the request size limit to 128 MB

Per the official Claude API release notes, on Oct 29 Anthropic launched the Usage and Cost Analytics API in beta, with two endpoints, /v1/organizations/analytics/usage and /analytics/cost, that filter and group token usage and cost by model, workspace, API key, and the user or service account behind each request, with 1-minute, 1-hour, or 1-day buckets. The older Usage and Cost API is now legacy but still supported. On Oct 28 the maximum request size for the Messages and Token Counting APIs on the first-party Claude API rose from 32 MB to 128 MB.

Key Takeaways

  • The new API needs the analytics-usage-cost-2026-08-24 beta header; one endpoint returns token and request counts, the other cost in USD (per the official docs)
  • group_by takes up to 5 dimensions, time buckets can be 1m, 1h, or 1d, and each filter accepts up to 100 values
  • Claude Console and Claude Enterprise organizations share the same endpoints and fields; it also works on Claude Platform on AWS and Claude Models on Google Cloud (Anthropic managed)
  • The old Usage and Cost API and four Enterprise Analytics usage and cost report endpoints are now legacy but still supported, with migration guides
  • Since Oct 28 the Messages and Token Counting APIs accept requests up to 128 MB instead of 32 MB (4x), on the first-party Claude API only
Read full story
ADSponsored

OpenAI launches Private Intelligence for enterprises: Zero Data Retention, Private Inference preview and agent approval controls

On Oct 30 OpenAI introduced Private Intelligence, a business privacy offering that bundles Zero Data Retention, a Private Inference confidential-computing preview for the API, agent access and approval controls, encryption and compliance certifications across ChatGPT, Codex and the API, built with some of its largest customers.

OpenAI launches Private Intelligence for enterprises: Zero Data Retention, Private Inference preview and agent approval controls

Key Takeaways

  • Covers three product lines, ChatGPT, Codex and the API, with per-product controls documented by OpenAI
  • Private Inference ships as an API preview that OpenAI describes as confidential computing with strict, verifiable controls
  • Businesses set agent data access, available tools, permitted actions and required approvals before agents act
  • OpenAI lists SOC 2 Type 2, ISO certifications, HIPAA BAAs and FedRAMP support; no benchmarks or pricing were published
Read full story

OpenCode 2.2.3 adds a built-in Claude Agent provider that runs Claude models through your own Claude Code login, subscriptions included

OpenCode, the open-source coding agent, shipped v2.2.3 at 09:55 UTC on Oct 30 with a built-in "Claude Agent" provider. After installing Claude Code and running claude auth login, users pick Claude Agent in /connect and can call Claude models through their own Claude Code login, whether that is a subscription, an API key or a cloud account. It runs the user's local claude program through Anthropic's public Claude Agent SDK, but turns off Claude Code's built-in tools and uses OpenCode's own tools and permission system instead. Hours earlier, OpenCode team member dax (@thdxr) teased on X "what if you could use your claude subscription in opencode", drawing about 2,300 likes.

Key Takeaways

  • v2.2.3 shipped at 09:55 UTC on Oct 30; its first highlight is "Use Claude Code in OpenCode", from PR #58782 (release notes)
  • Setup is three steps: install Claude Code, run claude auth login, and pick Claude Agent in /connect; OPENCODE_CLAUDE_PATH overrides the claude binary
  • The design spec says it uses only the public SDK, never reads Claude Code's saved login and never intercepts or changes its requests; subscription, API key and cloud logins all work (spec)
  • Claude Code's built-in tools, CLAUDE.md, hooks, MCP servers, skills, auto compaction and auto memory are all off; OpenCode's tools, permissions and compaction apply, with one claude process per session that stops after 5 idle minutes
  • dax's (@thdxr) teaser post drew about 2,300 likes and 280 replies (post); no performance comparison has been published
Read full story

ElevenLabs releases SFX v3 sound effects model: multi-part prompts, better music and vocals, now in ElevenAPI (limited access)

ElevenLabs' official account announced SFX v3 at 18:03 UTC on Oct 29, saying it creates richer, more precise sound effects from a single text prompt, with improved music and vocals, now available in ElevenCreative and ElevenAPI. Official docs list the model ID eleven_text_to_sound_v3 on the existing Sound Effects API, but v2 remains the default and v3 is in limited access and may not be available in every region.

ElevenLabs releases SFX v3 sound effects model: multi-part prompts, better music and vocals, now in ElevenAPI (limited access)

Key Takeaways

  • Model ID: eleven_text_to_sound_v3, on the same Sound Effects API as v2; switch by changing model_id, while eleven_text_to_sound_v2 stays the default
  • Docs list 4 improvements: several instructions in one prompt, music and vocal prompts, more natural output, and less clipping on high-frequency sounds like gongs, chimes and whistles
  • Limited access: accounts without access get an Unknown model error, and it may not be available in every region
  • Duration 0.1 to 30 seconds, billed at 40 credits per second when a duration is specified (Sound Effects endpoint rule), with loop for seamless looping
  • Benchmarks: no quantitative scores published; docs advise comparing v2 and v3 on your own prompts
Read full story
Linked Models & Agents:
⚡ SFX v3⚡ eleven_text_to_sound_v3🌐 ElevenLabs

GitHub Copilot Automations go team-wide: repo-owned shared automations, 24 new triggers, 20 new tools and AI credit limits

At GitHub Universe on Oct 28, GitHub shipped a batch of Copilot Automations updates. Shared automations now belong to the repository, act as Copilot, bill AI credits to the repo owner and keep running after their creator leaves. Automations gain 24 new GitHub event triggers, REST API triggers from external systems and chaining, plus 20 new tools and custom MCP servers. Teams can cap AI credits per run, day, week or month, automations can propose improvements to their own prompts from real outcomes, and VS Code 1.144 can run them in the cloud.

GitHub Copilot Automations go team-wide: repo-owned shared automations, 24 new triggers, 20 new tools and AI credit limits

Key Takeaways

  • Shared automations belong to the repository: anyone with read access can view them, anyone with write access can edit, actions are attributed to Copilot and AI credits bill to the repo owner
  • 24 new triggers across issues, pull requests, discussions and security alerts, plus REST API triggers from systems like Jira, Sentry and PagerDuty
  • 20 new GitHub tools (merge PRs, retry workflow runs, upload screenshots and more) and support for custom MCP servers
  • AI credit limits can be set per run, day, week or month; org repos need an admin to enable the "Allow use of Copilot cloud agent billed to the organization" policy
  • Rolling out to Copilot Pro, Pro+, Max, Business and Enterprise; VS Code 1.144 runs them in the cloud with chat.automations.cloud.enabled
Read full story

xAI adds a guidelines field to the Grok API for per-request moderation of NSFW, violence and profanity

On Oct 28 xAI announced on the official @grok account that all API customers can now add a guidelines array to text requests and set the nsfw, violence and profanity categories to decline, so Grok tightens that content while it writes rather than through after-the-fact filtering. The field works on the Responses API, Chat Completions and the xAI Python SDK; multi-agent models and deferred completions are not supported, and invalid values are rejected with 400 or 422 before the model runs.

xAI adds a guidelines field to the Grok API for per-request moderation of NSFW, violence and profanity

Key Takeaways

  • Three categories: nsfw, violence and profanity; the only behavior today is decline, and unlisted categories keep the default (docs)
  • Works on the Responses API, Chat Completions and chat.create in the xAI Python SDK; with the OpenAI Python SDK pass it via extra_body
  • Applies only to the request that carries it, so send it on every turn; the xAI SDK chat object stores it for you
  • Not supported on multi-agent models such as grok-4.20-multi-agent or deferred completions; duplicate categories return 400 and unknown values return 422
  • The official post had about 592K views and 271 likes when this was written
Read full story
Linked Models & Agents: