AI Coding Live News

Perplexity Launches Teammates: Persistent Work Agents on Computer with Slack, Research Preview for Max on Web

On November 5 Perplexity (@perplexity_ai) and CEO Aravind Srinivas (@AravSrinivas) launched Teammates: persistent personal agents on Computer that own ongoing responsibilities, inherit Projects, Skills, Memory and tool permissions, run in isolated desktops and browsers, collaborate with other teammates, and proactively monitor Slack. Official help docs call it a research preview for individual Max subscribers on the web; Enterprise Pro/Max are not included by default; native mobile and desktop apps are coming later.

Perplexity Launches Teammates: Persistent Work Agents on Computer with Slack, Research Preview for Max on Web

Key Takeaways

  • Shift from one-shot prompts to high-level goals; a teammate keeps its role until you reassign it (vendor/CEO)
  • Multi-agent orchestration, isolated desktop/browser, proactive monitoring of Slack/GitHub/Linear connectors
  • Individual Max (~$200/mo, ~10k Computer credits/mo) web research preview; Enterprise not included by default (help center)
  • Internal adoption: CEO says ~10k teammates in 30 days (~20x headcount) with ~1k active yesterday (personal claim, not an audit)
  • Setup on web: assign role and escalation, take over the desktop, or redirect; shared chats are read-only for others
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Claude Launches Personal Finance Beta in the US: Read-Only Bank Linking via Plaid, Up to 16 Banks and 24 Months of Transactions

On November 5 Anthropic announced a US beta of Personal Finance in Claude via @claudeai. Users link bank, card, loan and investment accounts through Plaid, see balances, cash flow, subscriptions and bills on one page, and ask questions in plain language. The official help article says access is read-only, so Claude cannot move, manage or invest money. It is limited to US Pro, Max and Ultra personal plans (not Free, Team or Enterprise) and is rolling out gradually.

Claude Launches Personal Finance Beta in the US: Read-Only Bank Linking via Plaid, Up to 16 Banks and 24 Months of Transactions

Key Takeaways

  • Read-only: balances, credit limits and up to 24 months of transactions; no money movement (vendor statement)
  • Up to 16 linked banks, US banks only; sign-in via Plaid so Claude never sees bank passwords
  • Pro, Max and Ultra personal plans only, on web, Desktop, iOS and Android, rolling out gradually
  • Chats inside the Personal Finance hub are not used for training; chats using account data are kept out of memory
  • Linked accounts cannot yet be used in Cowork, scheduled tasks or Claude Code
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Artificial Analysis: Tencent Hy4 Scores 35 on Intelligence Index v5, Ranked No.2 Among China Labs, 31% on FrontierCode 2.0

Independent evaluator Artificial Analysis published Tencent Hunyuan Hy4 results on the Intelligence Index v5 on November 5 (08:28 UTC): a composite score of 35, tying Grok 4.7 (xhigh) and one point behind Claude Haiku 5.5 (max). Among China-based labs it ranks second only to Kimi K3.1 Pro (46). On agentic coding it scores 31% on FrontierCode 2.0 and 37% on Terminal-Bench 4.0; cost per Intelligence Index task is about $0.78, roughly one-tenth of same-score Grok 4.7.

Artificial Analysis: Tencent Hy4 Scores 35 on Intelligence Index v5, Ranked No.2 Among China Labs, 31% on FrontierCode 2.0

Key Takeaways

  • Intelligence Index v5 composite 35: ties Grok 4.7 (xhigh), one point behind Haiku 5.5 (max, 36); No.2 China-lab model behind Kimi K3.1 Pro (46)
  • FrontierCode 2.0 at 31% (leads China labs; DeepSeek V4.1 Flash 1017 max 28%, Kimi K3.1 Pro 25%); Terminal-Bench 4.0 at 37%
  • AA-Omniscience v1.1 accuracy 50% but hallucination rate 89% (Kimi K3.1 Pro hallucination 37%); HLE-Diamond 33%
  • About $0.78 per Intelligence Index task: ~10x cheaper than same-score Grok 4.7, ~13x cheaper than Qwen3.8 Max (34), nearly 2x Haiku 5.5
  • Pricing about $0.834/$2.501 per 1M input/output tokens, cache reads $0.042; 1M context; text and image input
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Claude Code Background Subagent Status Lines Now Written by Haiku 5.5: About 99% Cheaper per Update, About 6% More Prompts per Limit

On November 4 ClaudeDevs, Anthropic's official developer account, said the status line that a background subagent in Claude Code refreshes every 30 seconds used to re-read its whole conversation each time, and is now written by Haiku 5.5 from short notes, so each update costs about 99% less. Anthropic says sessions with background subagents used about 5% less of the limit, giving about 6% more prompts on the same limit. It is live for most Pro, Max and Ultra users and everyone on Team and Enterprise, on v2.1.316 or later.

Key Takeaways

  • A background subagent refreshes its status line every 30 seconds, and each refresh used to re-read the subagent's whole conversation
  • Haiku 5.5 now writes the status line from short notes, which Anthropic says makes each update about 99% cheaper
  • The subagent itself keeps its own model; only the status line is written by Haiku 5.5
  • Anthropic says sessions with background subagents used about 5% less of the limit, so the same limit gives about 6% more prompts
  • It is live for most Pro, Max and Ultra users and everyone on Team and Enterprise; update to v2.1.316 or later
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Together AI Launches Together Compiler: Agents Continually Optimize Production Inference, Up to 2.6x Higher TPS

On November 4 Together AI launched Together Compiler (TKCC), which pairs a swarm of AI agents with a persistent inference engine to keep rewriting inference kernels for real traffic, models and hardware, as part of what it calls Polymorphic Inference. The company says that within 24 hours it reached up to 2.6x higher TPS on models such as Kimi K3, GLM 5.3, MiniMax M3.1-Flash and DeepSeek v4.1-Flash, up to 50% faster on customer workloads, and it already serves trillions of tokens a day in production.

Key Takeaways

  • Together Compiler recasts inference optimization from a classic compiler problem into an agentic search: agents propose kernel changes, a persistent engine checks speed and correctness, and only verified changes reach production
  • Together says that within 24 hours it reached up to 2.6x higher TPS on Kimi K3, GLM 5.3, MiniMax M3.1-Flash and DeepSeek v4.1-Flash, and up to 50% faster on customer workloads
  • On InferenceX low-latency batch size 1 it reports 1,111 TPS on MiniMax M3.1-Flash and 1,205 TPS on DeepSeek v4.1-Flash (company-reported); SemiAnalysis said it validated one of the results
  • The persistent engine hot-reloads candidate forward passes, cutting time to serving to 9 seconds versus 467 seconds for restarting an engine, with 252x higher experiment throughput than baseline agentic optimization systems
  • It already serves trillions of tokens a day in Together's production stack, where 40+ models are now optimized by agents
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Artificial Analysis Makes Time per Task Its Headline Performance Metric: GPT-6 Astra Finishes a Max-Effort Task in About 17 Minutes, Ahead of Opus 5.5 and Gemini 4 Carbon

Independent benchmarker Artificial Analysis announced on X on November 4 (17:34 UTC) a new version of its API performance benchmarking: the headline metric moves from output speed to Time per Task, the time to finish one Intelligence Index task (output tokens per task divided by end-to-end output speed). It covers about 1,000 serverless endpoints, measures speed from sending the request to the final token, and adds multi-turn agentic coding and knowledge-work workloads. Per its data, GPT-6 Astra at max effort runs at 51 tokens/s yet finishes a task in about 17 minutes, against about 26 for Claude Opus 5.5 (max) and about 29 for Gemini 4 Carbon (high).

Key Takeaways

  • Time per Task equals output tokens per Intelligence Index task divided by end-to-end output speed, so a model that is fast per token but verbose can finish later than a slower model that uses fewer tokens.
  • End-to-end output speed runs from sending the request to the final token, including the wait before the first token (prefill and hidden reasoning); output tokens now come from each provider's API-reported counts instead of the o200k_base tokenizer.
  • GPT-6 Astra (max) runs at 51 tokens/s with about 50k output tokens per task and takes about 17 minutes; Claude Opus 5.5 (max, 111 tokens/s) takes about 26 minutes and Gemini 4 Carbon (high, 71 tokens/s) about 29, using roughly 120k to 170k tokens per task.
  • At each lab's API default effort (medium for all three), GPT-6 Astra, GPT-6.1 Sol and Opus 5.5 cut Time per Task by about 3 to 5x and Cost per Task by about 2 to 4x, scoring about 5 to 11 points lower; Opus 5.5 drops from about 26 minutes and $10.04 to about 6.8 minutes and $2.45.
  • The official methodology page lists this as version 3.0.0 (October 30), with runs now every 6 hours and a new endpoint error rate metric.
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Cognition Launches SWE-2 Flash: A Fast Coding Model Built on Inkling-Small, 200 tok/s at One-Fifth the Price of SWE-2

Cognition released SWE-2 Flash on November 4 (19:55 UTC). Its official blog says the model is built on Thinking Machines' Inkling-Small (276B total, 12B active parameters) and, for the first time, adds a large mid-training stage before reinforcement learning, lifting FrontierCode 2.0 from 3.2% to 24.0% and Terminal-Bench 4.0 from 0.9% to 20.0%. Cognition says it beats every American open-weight model. It generates about 200 tokens per second at one-fifth the price and 3x the speed of SWE-2, and is free for Pro, Max and Teams users in Devin Desktop and CLI until the end of November. Weights are not public; a secure sharing program for partners and researchers is planned.

Cognition Launches SWE-2 Flash: A Fast Coding Model Built on Inkling-Small, 200 tok/s at One-Fifth the Price of SWE-2

Key Takeaways

  • SWE-2 Flash is built on Thinking Machines' Inkling-Small, with 276B total and 12B active parameters, about one-tenth the size of SWE-2 (2.8T-A104B).
  • Training adds a three-stage mid-training run on a few trillion tokens of scientific articles, blogs, code and other sources, followed by large-scale RL with higher-quality and more diverse tasks.
  • Official scores: FrontierCode 2.0 24.0 (SWE-2 scores 23.8), DeepSWE 1.1 60.1, Terminal-Bench 2.1 89.2, SWE-Bench Pro v2 Hard 89.9 and Terminal-Bench 4.0 20.0.
  • It runs at about 200 tokens per second versus about 60 for SWE-2, at one-fifth the price, and is free for Pro, Max and Teams users in Devin Desktop and CLI through November.
  • Citing cyber risk, the weights are not openly released; Cognition says details of a secure weight-access program will follow in the coming weeks.
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Linked Models & Agents:
⚡ SWE-2 Flash⚡ SWE-2⚡ Inkling-Small🤖 Devin🌐 Cognition🌐 Thinking Machines

Artificial Analysis Upgrades Its Six Capability Indexes to v2.0, Weighting Non-Hallucination and Agentic Tool Use; Claude Opus 5.5 Leads All Six

Independent evaluator Artificial Analysis announced on X on November 4 that its six Capability Indexes (Finance & Accounting, Strategy & Ops, Legal, Healthcare & Medical, Engineering, Economics) are now v2.0, aligned with Intelligence Index v5.0 evaluations and weighting Non-Hallucination and Agentic Tool Use more heavily. Across the indexes, HLE becomes HLE-Diamond, AA-Omniscience moves to v1.1 and GDPval-AA is removed; Engineering gains a new Science capability. Per Artificial Analysis, Claude Opus 5.5 (max) leads all six, with GPT-6 Astra (max) second in Finance & Accounting, Legal and Engineering. Among open-weights models, Kimi K3.1 Pro (weights not yet released) leads all six at #5 to #8.

Artificial Analysis Upgrades Its Six Capability Indexes to v2.0, Weighting Non-Hallucination and Agentic Tool Use; Claude Opus 5.5 Leads All Six

Key Takeaways

  • The six indexes map O*NET occupation tasks to representative benchmarks, weighted by how often each capability appears in that occupation (methodology page)
  • Common changes: HLE becomes HLE-Diamond, AA-Omniscience moves to v1.1, and GDPval-AA is dropped from Agentic Knowledge Work (now AA-Briefcase v1.1)
  • Non-Hallucination weight rises to 15% in Finance & Accounting and Legal; Agentic Tool Use (AutomationBench-AA) is 35% of Strategy & Ops, its largest component
  • Engineering adds Science (20%, CritPt v2 Hard Subset plus Terminal-Bench-Science 0.1) and FrontierCode 2.0 under Agentic Coding; Economics swaps AA-LCR for GDP.pdf
  • Results per Artificial Analysis: Claude Opus 5.5 (max) is #1 in all six; among open weights, Kimi K3.1 Pro leads all six, MiniMax-M3.1-Flash leads Strategy & Ops (#10) and DeepSeek V4.1 Flash 1017 (max) leads Healthcare & Medical (#12)
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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

Artificial Analysis releases AA-Omniscience v1.1: 3,000 refreshed questions and a calibrated 300-question public set, with Gemini 4 Carbon leading at a Net Score of 54

On October 31, independent evaluator Artificial Analysis released AA-Omniscience v1.1, its knowledge and hallucination benchmark and part of Intelligence Index v5. The question set shrinks from 6,000 to 3,000, dropping previously public and saturated questions, fixing errors and ambiguities, and adding new ones, and a new 300-question public set on Hugging Face is calibrated to the private set's difficulty, with 0.99 Pearson and Spearman correlation on Net Score. Gemini 4 Carbon (high) leads with a Net Score of 54, ahead of Claude Opus 5.5 at 48 and Claude Fable 5.1 at 46; Fable 5.1 has the highest accuracy (68%) and Gemini 4 Argon the lowest hallucination rate (4%). Models score about 1.9 points lower than on v1 on average, and MiniMax M3.1 Flash drops 22 points. All figures are as reported by Artificial Analysis.

Artificial Analysis releases AA-Omniscience v1.1: 3,000 refreshed questions and a calibrated 300-question public set, with Gemini 4 Carbon leading at a Net Score of 54

Key Takeaways

  • The question set shrinks from 6,000 to 3,000 across 6 domains and 42 topics: 600 each for science/engineering/math and software engineering, 450 each for finance, health, humanities/social sciences and law
  • A new 300-question public set is on Hugging Face, with 0.99 correlation to private-set Net Score and a median difference of 0.0 points
  • Net Score ranking: Gemini 4 Carbon (high) 54, Claude Opus 5.5 48, Claude Fable 5.1 46; the best open-weights model, Muse Spark Open, trails Gemini 4 Carbon by 28 points
  • Claude Fable 5.1 has the highest accuracy (68%), followed by Claude Opus 5.5 (66%); Gemini 4 Argon has the lowest hallucination rate (4%)
  • Rank order keeps a 0.98 Spearman correlation with v1, but scores are 1.9 points lower on average; MiniMax M3.1 Flash falls from 16 to -6 and MiMo V2.6 Pro from 8 to -5 (vendor-reported)
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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
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Linked Models & Agents:
⚡ Pika H3 Flex⚡ MiniMax H3⚡ Fal H3 Max⚡ HeyGen Video⚡ Gemini Omni 1.1 Flash🌐 Pika🌐 MiniMax

X discussion: Sam Altman calls DRAM sitting in personal PCs "quite terrible", and local-inference fans push back

On the Cheeky Pint podcast with Stripe co-founder John Collison, published October 29, Sam Altman discussed the memory shortage. He said many people assume open-source models and local inference solve the compute crunch, but from a global allocation perspective it is "quite terrible" for everyone to have scarce DRAM sitting in their own computer and being used about 5% of the time, and that a supply-constrained world wants maximum utilization in the cloud. A pubity post quoting him drew more than 26,000 likes and over 8 million views, and many users pushed back that PC memory is not only for AI and that local models matter for privacy and offline use. This is a summary of an X discussion, not a verified conclusion.

X discussion: Sam Altman calls DRAM sitting in personal PCs "quite terrible", and local-inference fans push back

Key Takeaways

  • Source: Sam Altman on the Cheeky Pint podcast, published October 29, with an official transcript that includes "being used 5% of the time"
  • Altman's argument: with DRAM supply tight, memory in personal computers sits mostly idle, while cloud data centers can serve many users continuously, so utilization should be maximized there
  • Engagement (X API, read Oct 31 13:06 SGT): the pubity post had 26,124 likes, 8,557 quotes, 5,210 replies and about 8.06 million views
  • Main pushback: PC memory also runs the OS, browsers and games; local models keep data on the device; some note the view matches OpenAI's cloud business model
  • Altman did not explain the 5% figure, and coverage suggests treating it as a rough estimate, not a measurement
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Perplexity CEO shares early Gemini 4 Carbon evals: a record 0.857 on the WANDR deep-research benchmark at $5.86 per task, ahead of Argon and GPT-6 Astra

Perplexity CEO Aravind Srinivas posted early evals of Gemini 4 Carbon on X: it scored a record 0.857 on Perplexity's Wide-And-Deep-Research (WANDR) long-form research benchmark at $5.86 per task, versus 0.802 ($7.88) for Gemini 4 Argon and 0.735 ($13.34) for GPT-6 Astra. He called Carbon the strongest model Perplexity has seen for long-form research and answer quality, and said it will come to all Pro and Max users inside Perplexity Computer once there is enough token capacity.

Perplexity CEO shares early Gemini 4 Carbon evals: a record 0.857 on the WANDR deep-research benchmark at $5.86 per task, ahead of Argon and GPT-6 Astra

Key Takeaways

  • WANDR scores (per Perplexity): Gemini 4 Carbon 0.857, Gemini 4 Argon 0.802, GPT-6 Astra 0.735, GPT-6.1 Sol 0.654, Claude Opus 5.5 0.647, Claude Fable 5.1 0.601
  • Cheaper too: Carbon costs $5.86 per task, below Argon at $7.88 and GPT-6 Astra at $13.34
  • Cost frontier in Perplexity's chart: DeepSeek V4 Pro 0813 (0.359, $0.75), GPT-6 Sol (0.566, $2.23), GPT-6.1 Sol (0.654, $3.70) and Carbon
  • Claude Sonnet 5.5 scored 0.550 ($2.73) and Grok 4.7 scored 0.528 ($9.30)
  • Not live yet: Carbon reaches Perplexity Pro and Max users only once token capacity allows, and Google still limits early access to cyber defenders in its Fairwind program
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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
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Claude renames its Max plans to Max and Ultra and removes the 5-hour limit for verified Ultra accounts

On Oct 30 Anthropic's official @claudeai account announced that Max 5x is now called Max (still $100/month) and Max 20x is now Ultra (still $200/month), with nothing for existing subscribers to do. Verified Ultra accounts also lose the rolling 5-hour usage limit starting that day, beginning with most existing subscribers; newer accounts keep a temporary limit until verified, and weekly limits are unchanged.

Claude renames its Max plans to Max and Ultra and removes the 5-hour limit for verified Ultra accounts

Key Takeaways

  • Prices are unchanged: Max is $100/month, Ultra is $200/month, and Pro is $20 billed monthly (per claude.com/pricing)
  • The pricing page lists Max at 5x Pro usage every 5 hours and every week, and Ultra at 10x Pro weekly usage with no 5-hour limit for verified accounts
  • Both tiers include monthly API credits, $100 on Max and $200 on Ultra, and Fable can use up to half the weekly limit
  • Newer accounts keep a temporary 5-hour limit until verified, which Anthropic says prevents fraud and abuse; users get an email when it is lifted
  • Weekly limits are unchanged; Anthropic has not published the verification criteria or timeline
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Linked Models & Agents:
⚡ Claude Fable⚡ Claude Opus⚡ Claude Sonnet🤖 Claude Code🌐 Anthropic