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Viết mã bằng AI Mới nhất

Viết code với AI: trợ lý lập trình, vibe coding, đánh giá mô hình code và những thay đổi trong cách phát triển phần mềm.

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Cập nhật 11 tháng 11 05:12

Viết mã bằng AI

11 tháng 11Thứ TưMục 1–20 trong 26
  1. Ars Technica · AI78

    Grok Build coding agent leaks confidential data from thousands of users, including SpaceXSI

    Security firm Claroc found that Grok Build, the vibe-coding agent from SpaceXSI, leaks proprietary business information and other sensitive secrets from thousands of users through shareable app links. Although Grok randomizes URLs so only people with the exact link can access the data, a design decision lets anyone enumerate thousands of live projects, and clicking those links generally reveals all associated data. The exposed material includes internal business plans, ad proposals, medical exam results, cruise-line galley checklists, and employee names and ID numbers, with some of it coming from 24 apps built by SpaceXSI itself, among them a confidential onboarding plan, a sales territory map of 594 accounts, and internal system addresses for sales tools.

    Vì sao đáng chú ý: Security firm Claroc's findings show how a URL design choice in Grok Build's sharing feature exposes thousands of live projects, including SpaceXSI's own internal documents.

  2. Google DeepMind88

    Google DeepMind releases Gemini 4 Carbon for agentic coding, cybersecurity and scientific reasoning

    Google DeepMind made Gemini 4 Carbon available to developers, enterprises, scientists and consumers, positioning it above Gemini 4 Argon for tasks that need deeper reasoning and more tokens. Carbon ranks #1 on CursorBench 4.0, Perplexity's WANDR and Zapier's AutomationBench at more than 2X lower cost per task than the next best external model, and is priced at $2 per million input tokens and $10 per million output tokens, the same introductory price as Argon. Google also previewed /swarm in Google Antigravity, which turns a single prompt into a coordinated team of agents, and reported that Carbon blocked 99% of attack trajectories in simulated cyberattack testing.

    Vì sao đáng chú ý: Google's release notes detail how Gemini 4 Carbon trades higher token usage for deeper reasoning, ranking first on CursorBench 4.0, WANDR and AutomationBench at under half the per-task cost of the next external model.

10 tháng 11Thứ Ba
  1. Latent Space85

    Anthropic releases Claude Fable 5.2 and Mythos 5.2, retaking the top LLM spot as its IPO roadshow begins

    Anthropic released Claude Fable 5.2 and Claude Mythos 5.2, which top most independent leaderboards, including Artificial Analysis Intelligence Index v5 at 65 and SemiAnalysis GDPx at 16.4%. Pricing drops 20% to $8/$40 per 1M input/output tokens, but SemiAnalysis reports cost per GDPx task rose from $67.16 to $88.15 because the model writes hundreds to thousands of test cases and spawns subagents to check its work. Multiple evals show the max effort level underperforming high, and Fable is capped at 50% of a weekly subscription limit.

    Vì sao đáng chú ý: Anthropic releases Fable 5.2 and Mythos 5.2 with a 20% per-token price cut, yet independent evals show higher per-task cost and a max-effort setting that underperforms high.

  2. Claude YouTube78

    Anthropic releases Claude Fable 5.2 for ambitious technical work

    Anthropic released Claude Fable 5.2, a model designed for the most daunting technical challenges and ambitious open-ended projects, with improvements in coding, reasoning and vision. The company recommends starting with Opus 5.5 for most coding and knowledge tasks; Fable 5.2 is available today on Pro, Max, Team and Enterprise plans.

    Vì sao đáng chú ý: Claude Fable 5.2 targets ambitious open-ended technical work, with stated gains in coding, reasoning and vision, while Opus 5.5 remains the recommended default for most coding and knowledge tasks.

9 tháng 11Thứ Hai
  1. Meta AI Research Blog78

    Meta releases Muse Ember, its smallest and most efficient model for agent tasks

    Meta released Muse Ember, its smallest and most efficient model, aimed at the repetitive work agents do all day such as tool calls, coding subtasks, routing, extraction and long document parsing. Meta says it matches Claude Haiku 5.5's best score at about a third of the cost and beats every GPT-6 Luna setting, at $0.10 per million input tokens and $0.50 per million output tokens. It ships with a 1M-token context window, up to 512K output tokens, native text, image, audio and video input, and is available today on Muse Code and the Meta Model API as muse-ember-1.0.

    Vì sao đáng chú ý: Meta's smallest model targets the high-volume agent steps, and its cost-per-task comparison against Claude Haiku 5.5 and GPT-6 Luna shows where the efficiency claim comes from.

  2. Every78

    Every review: Fable 5.2 can build a full product with little steering

    Every reports that Fable 5.2, Anthropic's newest model, let Kieran Klaassen build a video editor combining Adobe Premiere, After Effects and Ableton-style features, with an AI agent running it, and Everycut went live three days after he started. The review says Fable 5.2 can take a whole product and keep building, testing and refining until it is usable daily, unlike Opus 5.5, which keeps the user checking its work, and Fable 5 and 5.1, which run large tasks for hours or days. Every calls it the first model it has tested that lets professionals build production-grade tools without reading the underlying code.

    Vì sao đáng chú ý: Every's hands-on account of Fable 5.2 shows how far a single model can carry a product build with minimal steering, offering a concrete comparison against Opus 5.5 and Fable 5/5.1.

6 tháng 11Thứ Sáu
  1. Google Developers Blog60

    Google study finds stale TODO bug references distract coding agents in 13% of sessions

    ) references. Agents acted on the bug ID in 6% of encounters, and 83% of those actions were unprompted by the developer; 41% of unprompted detours chased already closed tickets while 57% hit still-open bugs and expanded scope. Controlling for developer, codebase complexity, task type, model tier and other factors, unprompted bug detours added 70% more steps, 77% more tokens and 105% more wait time per session.

    Vì sao đáng chú ý: Google's large-scale trace analysis shows stale TODO bug references pull coding agents into unprompted detours, and its cleanup agents removed thousands of obsolete references with over 95% reviewer acceptance.

5 tháng 11Thứ Năm
  1. Artificial Analysis Articles78

    Thinking Machines releases Inkling 2 Small, scoring 31 on the Artificial Analysis Intelligence Index

    Thinking Machines released Inkling 2 Small, a proprietary reasoning model that scores 31 on the Artificial Analysis Intelligence Index v5.0.1, 18 points above Inkling Small, with Apache 2.0 weights planned for a later, unconfirmed date. The 594B-parameter model (17B active) is available first through Tinker and Tinker Playground at $0.30/$1.00 per 1M input/output tokens, and it matches Gemini 3.8 Flash (high) while trailing Claude Sonnet 5.5 on coding and hallucination benchmarks. It averages about 150K output tokens per Intelligence Index task, roughly 4x Inkling Small, at $1.18 per task versus $0.20.

    Vì sao đáng chú ý: Artificial Analysis's independent benchmark run shows where Inkling 2 Small gains over its predecessor and where it still trails Gemini 3.8 Flash and Claude Sonnet 5.5.

29 tháng 10Thứ Năm
  1. Mistral AI86

    Mistral releases open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter multimodal model

    Mistral AI released the open weights of Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that entered public preview earlier this month and was trained from scratch in Europe. The company says three additional weeks of reinforcement learning lifted it to the top of open-weight coding, with 41% on Terminal-Bench 4.0 and 48% on the Artificial Analysis Coding Agent Index while using 47% fewer tokens per task than GLM-5.3. It adds Low, Medium and High reasoning modes, scores 51% on the Artificial Analysis Cyber Index, and is priced at $0.70 input, $0.07 cached input and $2.10 output per million tokens under a modified MIT license.

    Vì sao đáng chú ý: Mistral opens the weights of its 1-trillion-parameter flagship, pairing coding and defensive-security benchmark gains with three reasoning modes and per-token pricing.

  2. Google DeepMind82

    Google DeepMind announces Gemini 4 Carbon, a frontier model focused on cybersecurity and long-horizon tasks

    Google DeepMind announced Gemini 4 Carbon, its strongest model yet, with state-of-the-art cybersecurity and long-horizon capabilities, ranking #1 on the v5.0 Artificial Analysis Intelligence Index at 59. It scores 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching, and reaches #4 (1768) on WebDev Arena. Carbon launches at the same introductory price as Argon, $2 per million input tokens and $10 per million output tokens, and is first available to a subset of cyber defenders through the Fairwind Program before wider release.

    Vì sao đáng chú ý: Gemini 4 Carbon's benchmark results in vulnerability discovery and patching show how frontier models are being positioned for cybersecurity defense work.

28 tháng 10Thứ Tư
  1. Artificial Analysis Articles82

    Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5, ranks second on Coding Agent Index

    Google's Gemini 4 Carbon is the new No. 5 on the Artificial Analysis Coding Agent Index. 5 (max, 62), and it leads 4 of the 9 Index evaluations including HLE-Diamond and Terminal-Bench-Science. 1 rises from 4% to 11%; the model is being rolled out to selected users and is not yet publicly available.

    Vì sao đáng chú ý: Gemini 4 Carbon tops Artificial Analysis Intelligence Index v5 at 59 and trails Claude Opus 5.5 by 3 points on the Coding Agent Index, with per-task cost and hallucination figures for comparison.

27 tháng 10Thứ Ba
  1. Replit Blog75

    Replit Cloud Agents now let non-engineers contribute to company codebases

    Replit launched Cloud Agents for existing repositories, letting anyone on a team describe a change in plain language and have the agent open a tested pull request in GitHub or GitLab, available to all Pro and Enterprise customers. The agent runs each change in isolated cloud sandboxes, clicks through the running app, and attaches screenshots and video to the PR, while shared environments give every contributor a preconfigured setup that updates from the main branch. Replit also introduced early-access self-improving workspace memory, which turns review comments into team skills that engineers approve, and a Slack integration that lets users mention @Replit in a thread to start a change.

    Vì sao đáng chú ý: Replit 把 Cloud Agents 接入企业既有代码库，让非工程人员以带测试视频的 PR 形式提交改动，展示了 AI 编码工具向团队协作流程延伸的一种路径。

19 tháng 10Thứ Hai
  1. MiniMax Blog83

    MiniMax releases M3.1 Flash, claiming 70% lower cost and nearly 3x faster inference

    MiniMax officially rolled out MiniMax M3.1 Flash, a next-generation Flash model that it says cuts usage cost by 70% versus its predecessor and makes online inference nearly three times faster. The company reports 73.7% on DeepSWE v1.1, 76.2 on BankerToolBench, 61.7 on OSWorld V2, 29.6 on Agents' Last Exam and 82% on CyberGym, with output speeds of 200+ TPS and a median of 170+ TPS. Pricing is $0.15 per 1M input tokens, $0.60 per 1M output tokens and $0.015 per 1M cached input tokens, and the model is available through the MiniMax API, M Plan and MiniMax Code, with weights to be released later.

    Vì sao đáng chú ý: MiniMax's Flash model claims top-tier agentic and coding scores at $0.15 per 1M input tokens, offering a concrete data point on the cost side of agent workloads.

15 tháng 10Thứ Năm
  1. Anthropic · Claude.dev62

    How the Claude Code team does code review

    The Claude Code team describes its layered code review flow, in which most reading is done by machines and human attention is reserved for risk-based approval. From June to September, merged pull requests to the Claude Code codebase more than doubled while the share that had to be reverted fell by more than 40 percent. The process stacks verification before the PR, required CI, Claude review, security review, risk-tiered human approval and a merge queue with flags and fast rollback, and the team turned past incidents into a daily eval set for the reviewer.

    Vì sao đáng chú ý: Anthropic's Claude Code team details how it moved most code reading to automated layers while merged PRs doubled and reverts fell over 40 percent, offering a transferable review model for agent-heavy teams.

  2. Meta AI Research Blog88

    Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

    Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

    Vì sao đáng chú ý: Meta releases open weights for an 816B-parameter multimodal model that runs on two Mac Studios, giving developers a local option for long-horizon coding and agentic tasks.

  3. Modal Blog78

    Meta's Muse Spark Open weights released and available on Modal Auto Endpoints

    Meta released the weights for Muse Spark Open, and Modal now serves the model through its Auto Endpoints with per-token billing. The model targets agentic coding and long-horizon tasks, with native multimodal understanding, a 1M-token context window, multi-step planning, sequential tool use and failure recovery. Meta reports 76.4 on Terminal-Bench 2.1 with the Terminus 2 agent and 60 on DeepSWE 1.1 using its preferred harness at xhigh reasoning effort.

    Vì sao đáng chú ý: Meta's open-weight Muse Spark Open is now served on Modal, giving teams a per-token way to try an agentic coding model with a 1M-token context window.

12 tháng 10Thứ Hai
  1. Meta AI Research Blog82

    Meta releases Muse Wonder and Muse Wonder Full frontier models for personal and professional agents

    Meta introduced the Muse Wonder series, comprising Muse Wonder and Muse Wonder Full, which it says delivers state-of-the-art performance on personal and professional agentic tasks, memory and coding. Muse Wonder is available today through the Meta Model API and Muse Code, while Muse Wonder Full is available today for Muse Code subscribers. The models pair Active Memory, which carries context across conversations and retrieves task-relevant details, with Active Focus, which sustains work on long-running goals and checks in before important actions; Meta says both were trained at its gigawatt-scale Prometheus data center.

    Vì sao đáng chú ý: Meta's Muse Wonder series claims state-of-the-art results on agentic, memory and coding benchmarks, with Active Memory and Active Focus as the two capabilities it is built around.

6 tháng 10Thứ Ba
  1. Mistral AI82

    Mistral launches Mistral Large 4 preview, a 1T-parameter multimodal model with weights due this month

    Mistral has launched a public preview of Mistral Large 4, a 1 trillion-parameter natively multimodal model with 52 billion active parameters, available now through the Mistral Studio API. Mistral says the model is competitive with the strongest open-source models and outperforms other open-weight models from the US and Europe, scoring 82% on an Artificial Analysis Cyber Index vulnerability reproduction and patching test and 49.8% on its combined Coding Agent Index. The weights are set to be released by the end of the month, after red-teaming with cybersecurity partners and authorities; Mistral says the model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own European datacenters.

    Vì sao đáng chú ý: Mistral's largest model yet claims open-weight leadership in cybersecurity and agentic coding, with weights promised by month's end.

30 tháng 9Thứ Tư
  1. Factory62

    Factory makes Automations generally available for Droid agents

    Factory has made Automations generally available, letting teams describe a recurring workflow and have Droid run it on a schedule or event trigger. Each automation combines a trigger (schedule, Slack, GitHub, or webhook), instructions, a user-chosen model and reasoning level, and a machine that can be local, connected, or a Factory-managed Droid Computer. Factory says 52% of enterprise users ran automated code review in the past 30 days, 48% used security review, 35% used AutoWiki, and automation session volume grew 51% over the prior 30 days.

    Vì sao đáng chú ý: Factory makes its Automations generally available, showing which recurring engineering workflows enterprises actually hand to Droid and how model and machine choice work per task.

28 tháng 9Thứ Hai
  1. Anthropic · Claude.dev82

    Anthropic releases Claude Sonnet 5.5 with unchanged per-token pricing and 30% faster output

    Anthropic released Claude Sonnet 5.5, the second model in the Claude 5.5 family after Opus 5.5, which the company says is smarter, more efficient and 30% faster than Sonnet 5 at the same per-token price. Because it typically needs fewer tokens per task, Anthropic says most work costs up to 30% less, and the model ships with a native 1M-token context window, 128k max output and thinking on by default. The release guide lists breaking changes for Sonnet 5 users, including replacing disabled thinking with the between_tools setting, swapping forced tool_choice for auto plus strict tools, and moving computer use to the computer_toolset_20260801 toolset.

    Vì sao đáng chú ý: Anthropic's Sonnet 5.5 keeps Sonnet 5 pricing while cutting per-task token use, and the migration notes show what changes for teams already building on Sonnet 5.