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AArtificial Analysis full article10:32First-partyAnalysis

AI score66

Artificial Analysis releases Intelligence Index v5, with 60% of evaluations switched to private datasets

Artificial Analysis releases Intelligence Index v5, adding two new benchmarks, FrontierCode 2.0 and Terminal-Bench-Science, upgrading HLE-Diamond, AA-Omniscience v1.1, and CritPt v2, with 60% of evaluations based on private test sets or held-out answers. Claude Opus 5.5 (max) ranks first with 58 points, followed closely by GPT-6 Astra (max) at 57 points and GPT-6.1 Sol (max) at 55 points, with Gemini 4 Argon (high) at 50 points. The gap between open-weight models and the leaders has widened from 3 points to 12 points; the released-weight DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) both score 32 points. The per-task cost of leading models differs by more than 13 times: GPT-6.1 Terra (max) is $0.96, while Claude Fable 5.1 (max) is $13.04. A Progress Index v1 was also released, placing models released since November 2022 on the same capability scale for comparison.

Same story · 8 reports

#Evaluations / benchmarks#Agents#English text is being prepared.

AArtificial Analysis10:31Analysis

AI score65

Artificial Analysis releases Intelligence Index v5, with 60% of evaluations based on private datasets

Artificial Analysis releases Intelligence Index v5, adding two new benchmarks, FrontierCode 2.0 and Terminal-Bench-Science, upgrading HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, removing GDPval-AA v2.1, SciCode and AA-LCR v1.1, and changing 60% of the index to evaluations based on private test sets or held-out answers. It also launches Progress Index v1, which uses a unified scale to track changes in model capabilities over time. The results show that Claude Opus 5.5 (max) ranks first with 58 points, followed by GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55), while Gemini 4 Argon (high) scores 50; the gap between open-weight models and the leaders has widened from 3 points to 12 points, and among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead with 32 points. The per-task cost of the top ten models differs by more than 13 times, with GPT-6.1 Terra (max) lowest at $0.96 and Claude Fable 5.1 (max) highest at $13.04.

Same story · 8 reports

#Evaluations / benchmarks#Agents#English text is being prepared.

AA Yi AI Notes09:27

AI score55

The author tested an email Agent on 5,039 emails and includes 5 scenario prompts

The author ran their own QQ Mail inbox of 5,039 emails through the email Agent on mobile; of 69 emails from the past month, about 20 needing action were filtered out in 43 seconds. The article provides prompts for 5 common scenarios, plus a memory template of "user profile + 10 behavioral guidelines," usable by copying without writing any code. Core scenarios include finding hidden AI auto-charges and SaaS renewals in 17 seconds, and merging multiple banks' Chinese and English monthly statements and calculating foreign currencies separately in 30 seconds.

Quoted postAYi · @AYi_AInotes

https://t.co/BSzz78Spop

#Tutorials / practice#Agents#English text is being prepared.

SShao Meng09:27

AI score64

Blake Anderson open-sources ai-app-playbook, using a Coding Agent to build consumer-grade apps on a fixed stack…

Blake Anderson has open-sourced "ai-app-playbook," an operating playbook for coding agents to execute, covering product selection, building, launch, and first-month profitability. It has two layers: a human-facing playbook/00–13 chapters and an Agent-facing AGENTS.md (Phase 0–14, with each step marked [AGENT] or [HUMAN]); before starting, one must pass the Gate A scoring. The tech stack is fixed on Cloudflare Workers, D1, Better Auth, OpenRouter routing to DeepSeek, Superwall plus Stripe, etc., and explicitly lists a long set of non-choices such as Vercel, Supabase, and Next.js.

Quoted postBlake Anderson · @blakeandersonw

Here's my current tech stack for scaling consumer apps in 2026. It's by far the best & cheapest stack for agent-native development ⬇️ https://t.co/0yqdwDeCp5

#English text is being prepared.#Agents#English text is being prepared.

VVaibhav Srivastav09:27Analysis

AI score53

GPT-6.1 Terra beats Astra on vision benchmarks, roughly 47% faster and about 92% cheaper

Vaibhav Srivastav, citing SkalskiP's vision benchmark results, said that GPT-6.1 Terra beats Astra on text, object detection, and counting tasks, while also being roughly 47% faster and about 92% cheaper. The original post includes leaderboards and example links.

Quoted postSkalskiP · @skalskip92

GPT-6.1 Terra beats Astra in our vision benchmarks wtf OpenAI? had to double-check the results - text - object detection - counting also ~47% faster and ~92% cheaper ↓ leaderboards and examples https://t.co/4AvboaD16g

#Evaluations / benchmarks#Multimodal#OpenAI

LLarsen Cundric09:27

AI score57

Agree on verifiable completion criteria before writing code with agents

Larsen Cundric proposes a rule for writing code with agents: before requesting a change, first agree on how both sides will confirm the change works. His acceptance methods include a test that currently fails, a command you can run yourself, and logs or screenshots of real run results; a "done" without evidence will be sent back. He believes most failed agent sessions start with vague requirements and missing checks, and he asks readers to share their own criteria for trusting an agent's "done."

#Tutorials / practice#Agents#English text is being prepared.

DDex Horthy09:27Analysis

AI score52

SlopCodeBench Retest: GPT-6 Astra Leads with 16.3% Strict Pass Rate, Ahead of Opus 5.5

The author retested frontier models using the full version of SlopCodeBench (36 questions, 196 checkpoints per setting). The highest was GPT-6 Astra with a strict pass rate of 16.3%, above GPT-5.5's 14.8% in May, with the tested models overall ranging from 12.2% to 16.3%. In the three full comparisons, the XHigh tier cost 2.11-4.22 times that of Medium, but Opus 5.5 actually had more strict passes at the Medium tier (28 vs. 25), while Astra and SOL performed better at the XHigh tier. Regarding code quality, Opus 5.5 XHigh had a slop line proportion of 77.75%, lower than Medium's 94.25%; the author cautions that this may be because the new models produce new anti-patterns not yet covered by the 200-plus linters.

Same story · 2 reports

#Evaluations / benchmarks#English text is being prepared.#Agents

EEthan Mollick09:27Analysis

AI score62

Ethan Mollick tests Genie 4 generating game scenes in real time

Ethan Mollick tested Genie 4 and said it can generate game-like environments while generating, and that entering "Neogothic brutalist city inhabited by robots and horses" produces a scene in about one minute. He then asked for a dragon and a friendly otter NPC to be added, and these changes were all made in real time. He said the experience is not perfect.

Same story · 2 reports

#English text is being prepared.#Multimodal#Video

AArena09:27Analysis

AI score60

GPT-6.1 Terra ranks 14th on Code Arena WebDev with 1,671 points and a 28% lower cost per task

Arena.ai announced that OpenAI's GPT-6.1 Terra (Max) ranks 14th on Code Arena: WebDev with 1,671 points, 151 points higher than GPT-5.6 Terra, which now ranks 56th. Its median cost per task is $0.40, 28% lower than GPT-5.6 Terra's $0.56. Within the same family, it is 81 points below the 4th-ranked GPT-6.1 Sol (1,752 points), but its median cost per task is 75% lower; it is 6 points below the 11th-ranked GPT-6 Sol (1,677 points), with a 76% lower cost.

Quoted postOpenAI · @OpenAI

GPT-6.1 Terra: delivering fast and capable computer use at a lower price. GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it. https://t.co/8JTN1yonmV

#Evaluations / benchmarks#English text is being prepared.#OpenAI

UUnite.AI09:26Analysis

AI score58

Perplexity Publishes LLM Gateway Best Practices Guide, Outlining Request Flow and Production Risks

On October 27, 2026, Perplexity published an LLM gateway best practices guide explaining how this middleware layer routes, authenticates, caches, and logs model calls, and distinguishing four categories: API gateways, LLM gateways, MCP gateways, and AI gateways. The guide breaks down the mechanism through a six-step request lifecycle, while warning that the gateway centralizes credentials from various providers, and cites the LiteLLM gateway breach disclosed by Microsoft and two high-severity vulnerabilities, CVE-2026-35029 and CVE-2026-59823, reported by Check Point (already fixed in LiteLLM 1.83.9). The article also lists postmortems of Cloudflare AI Gateway and OpenRouter outages, four deployment paths, and paid plans starting at about $50 per month.

#Safety / alignment#English text is being prepared.#OpenAI

LLeikeji09:26Analysis

AI score52

Rokid Glasses 2 First-Hand Experience: More Comfortable to Wear, AI and Battery Life Upgraded

Leikeji experienced the Rokid Glasses 2, released on October 27, in advance. It starts at 3,699 yuan (3,199 yuan after national subsidy), and officially goes on sale on November 11. Compared with the previous generation, the new model's temples use a more skin-friendly material to distribute pressure, the battery capacity has increased to 263mAh, up 25%, and it is equipped with a 12-megapixel camera supporting 4K 30fps and 2K 60fps video recording. In the author's test, after 20 minutes of navigation, 30 minutes of music, and 30 minutes of photo and video recording, more than half of the battery remained, and magnetic charging can reach 75% in 20 minutes.

Same story · 7 reports

#Product updates#English text is being prepared.#Evaluations / benchmarks

NNVIDIA Developer · Generative AI09:24First-partyAnalysis

AI score52

NVIDIA Explains How to Fine-Tune Nemotron 3.5 Lightning with the Nemotron-Personas Dataset

NVIDIA has released the Nemotron-Personas France and Belgium datasets and provided a complete post-training workflow for fine-tuning Nemotron 3.5 Lightning with them. The workflow includes defining behavior contracts, generating synthetic conversations with personas, fine-tuning with LoRA on NVIDIA NeMo, merging adapters, and then comparing and evaluating item by item against the base model on a frozen test set. The France dataset was built in collaboration with Pleias, and the Belgium dataset involved Pleias and KU Leuven; the Nemotron repository provides reusable steps and agent skills for data generation, training, and evaluation.

#Tutorials / practice#English text is being prepared.#Open-source ecosystem

LLangChain Blog09:22First-partyAnalysis

AI score67

How LangChain Built the Design Agent Content Studio

LangChain's GTM engineering team, working with the design team, built Content Studio, a design agent inside Slack, on Managed Deep Agents. It can generate videos, social graphics, UI mockups, and slide decks, and has already produced more than 3,000 assets and over 80 slide decks. The article breaks down its four-layer knowledge structure (skills, wiki, code validation, memory), the dedicated LangSmith Sandbox for each Slack thread, the two rendering engines HyperFrames and the in-house motion-studio, and how the main agent orchestrates asynchronous sub-agents. The team says that after migrating to Managed Deep Agents, its own code shrank by about 80%, the median model cost per request fell from about $8 in mid-September to about 50 cents, and the entire harness has been open-sourced.

Same story · 2 reports

#Tutorials / practice#Agents#English text is being prepared.

FFull Artificial Analysis article08:00First-partyAnalysis

AI score82

Gemini 4 Carbon Tops the Artificial Analysis Intelligence Index v5

Google's Gemini 4 Carbon ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59, leading in four of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. On the Coding Agent Index, Gemini 4 Carbon (high) paired with the Antigravity CLI scores 59, trailing Claude Code paired with Claude Opus 5.5 (max, 62 points) by 3 points. The model has a 1M-token context window, supports text, image, video, and speech input, and is priced at a standard rate of $4/$20 per million input/output tokens; it is currently available to select users with a 50% discount.

Same story · 34 reports

#Evaluations / benchmarks#Google#Reasoning

KKarl's AI Watts21:51Analysis

AI score58

The author's hands-on record of connecting 15 mainstream apps to Ant Lingguang 2.0

After getting access to the Ant Lingguang 2.0 beta, the author connected 15 domestic apps—including Mijia, Baidu Netdisk, DingTalk, Didi, Luckin, McDonald's, Amap, Fliggy, Alipay, and Taobao—to this Personal Agent through methods such as QR code authorization, MCP, Skill, GitHub projects, and CLI. The article explains the integration method for each app one by one according to configuration difficulty, and provides a reusable configuration prompt that requires connecting only one service at a time, verifying with real queries after connecting, and confirming before executing any action that involves spending money. The author believes that the mobile Personal Agent differs in form from the desktop version, with its advantage lying in its ability to connect to domestic services, and that it will arrange scheduled tasks on its own to continuously follow up in the background.

#Tutorials / practice#Agents#English text is being prepared.

AAlibaba Cloud Developer Community AI19:01Analysis

AI score60

GEO Practical Guide: AI Health Self-Check, Content Structuring Overhaul, and Building Trustworthy Signals Across the Web

The Alibaba Cloud Developer Community has released a GEO practical guide, offering a complete implementation path from AI health self-check to content overhaul, trustworthy signal building, and monthly monitoring. The article recommends selecting 3–5 mainstream AI tools (such as ChatGPT, ERNIE Bot, Doubao, Kimi, and Tencent Yuanbao) and using standardized questions to test brand mentions and recommendation status as an optimization baseline. The content overhaul section proposes four types of approaches—structuring, factualization, Q&A formatting, and de-marketing—and lists five common misconceptions, including treating GEO as SEO and optimizing only the official website.

#Tutorials / practice#Search#English text is being prepared.

AAlibaba Cloud Developer Community AI19:01Analysis

AI score53

Hands-on test of Alibaba Cloud Bailian's built-in web search and web scraping: enable with a one-line tools declaration

The author tested on Alibaba Cloud Bailian 2.5.0. After declaring web_search via the tools parameter, the model went from refusing to answer "today's weather in Shanghai" in 9.2 seconds to returning the day's actual conditions in 24.5 seconds with the correct date. Adding web_extractor lets the model decide the number of retrieval rounds and scraping targets on its own; in the JSON output, the counts of two web_search calls and three web_extractor calls match the per-call records. The author also recorded web-enabled call latency ranging from 17 to 127 seconds and 22,789 input tokens in a single call, and notes that the agent and agent_max strategies other than turbo incur an additional charge per call.

#Tutorials / practice#Agents#English text is being prepared.

SSaito Hardcore Hacker18:15Analysis

AI score68

npm founder Laurie Voss on how to evaluate whether an AI agent is up to standard

Laurie Voss, co-founder of npm and now head of developer relations at Arize AI, presented a method at the AI Engineer New York workshop for judging whether an AI agent is up to standard: read through a dozen or so trace records before writing the first evaluation, and evaluate outcomes rather than steps. He used a research report that added two search results together into a 116% analyst consensus as an example to show that only by looking at the records can you pinpoint which step the reasoning went wrong at; after switching from a correctness evaluation to a faithfulness evaluation, the same batch of 16 reports went from all being judged wrong to 14 being judged unfaithful with usable explanations. He also required testing the judge itself with a manually labeled golden dataset, and writing the stock ticker correctness as a floor and operability at an average of 0.8 over three runs as a target into npm test.

#Tutorials / practice#Agents#Evaluations / benchmarks

SShao Meng18:13

AI score69

Open-source meta-skill skill-diet uses evaluation to slim down Agent Skills

The open-source meta-skill skill-diet is used to compress bloated SKILL.md files and check whether tools are outdated. The author @nicopreme measured on real skills that it can reduce tokens by 70–80% with unchanged results. A slimmed-down version is kept only if it is at least on par in same-task evaluations against the old skill, the new skill, and no skill; every subsequent addition must also pass the threshold, and a roughly 10% increase over the previous diet triggers another round. It also places information in layers by skill description, SKILL.md, references/, and tool help and error messages, and recommends leaving mechanical errors to error messages, tests, or linters.

Quoted postNico Bailon · @nicopreme

I've been dogfooding "skill-diet", a skill for slimming down agent skills and keeping them correct. most skills load far more text than a task needs, and some parts often go stale. it also gets worse over time as AI updates skills. skill-diet cuts what the model already does without being told, moves rarely needed detail into files it only reads when needed, and fixes what no longer matches the tool. then it runs the same tasks with the old skill, the new one and no skill, and keeps the new one only if agents do at least as well. on my own skills: 70–80% fewer tokens, same results. https://t.co/h1BnVt7NjD

#English text is being prepared.#Agents#Evaluations / benchmarks

WWired AI18:02Analysis

AI score61

WIRED tested six always-on AI agents for a day, and OpenAI Dots had the highest overall score

WIRED reporter Reece Rogers spent a day testing six always-on AI agents on an iPhone—Meta Muse, OpenAI Dots, Instinct, Anthropic Claude Duet, Google Gemini Spark, and xAI Grok Bot—scoring them from 1 to 5 across five tests: a morning news briefing, ordering lunch, a calendar conflict reminder, a travel itinerary, and a late-night task. OpenAI Dots performed best overall. Dots costs $100 per month, making it the most expensive in this test, and the author suggests most people wait for a price cut or a free version before trying it. The free and easy-to-use Meta Muse came right behind in total score, Anthropic Claude Duet was rated as having the best interface, and Instinct was rated as the most annoying.

#Evaluations / benchmarks#Agents#OpenAI