[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-claude-code-leak-reveals-advanced-agentic-architec-summary":3,"summaries-facets-categories":87,"summary-related-claude-code-leak-reveals-advanced-agentic-architec-summary":3672},{"id":4,"title":5,"ai":6,"body":13,"categories":63,"created_at":65,"date_modified":65,"description":56,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":68,"navigation":69,"path":70,"published_at":71,"question":65,"scraped_at":65,"seo":72,"sitemap":73,"source_id":74,"source_name":75,"source_type":76,"source_url":77,"stem":78,"tags":79,"thumbnail_url":65,"tldr":84,"tweet":65,"unknown_tags":85,"__hash__":86},"summaries\u002Fsummaries\u002Fclaude-code-leak-reveals-advanced-agentic-architec-summary.md","Claude Code Leak Reveals Advanced Agentic Architecture",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",6295,1366,9201,0.0019195,{"type":14,"value":15,"toc":55},"minimark",[16,21,34,38,41,45,48,52],[17,18,20],"h2",{"id":19},"source-map-misconfig-exposed-full-claude-code-source","Source Map Misconfig Exposed Full Claude Code Source",[22,23,24,25,29,30,33],"p",{},"Publishing minified TypeScript to npm without excluding source maps (.map files) leaked Claude Code's entire codebase. Bun bundler generates maps by default; forgetting ",[26,27,28],"code",{},"*.map"," in ",[26,31,32],{},".npmignore"," or disabling them publishes a map linking to uncompressed src.zip on a public Cloudflare R2 bucket. Result: 1,906 files, 512,000+ lines downloadable via curl. Security researcher Chaofan Shou (@Fried_rice) discovered it March 31, 2026; archived GitHub repo gained 1,100 stars, 1,900 forks. This mirrors prior Mythos CMS leak (3,000 docs public due to default config), highlighting need for explicit security boundaries in defaults.",[17,35,37],{"id":36},"hidden-features-show-proactive-agent-evolution","Hidden Features Show Proactive Agent Evolution",[22,39,40],{},"BUDDY, a feature-flagged Tamagotchi AI pet, seeds from user ID hash with 18 species (duck to legendary ghost), rarity tiers, cosmetics, and stats (DEBUGGING, PATIENCE, CHAOS, WISDOM, SNARK). Claude generates name\u002Fpersonality on hatch with animations; planned April 1 teaser for employees. KAIROS (\"Always-On Claude\") persists across sessions via private dir memory logs, runs nightly \"dreaming\" to consolidate context (handles midnight boundaries), and proactively initiates tasks. ULTRAPLAN enables 30-min cloud planning. These push agents beyond invocation to always-on collaborators, with production edge-case handling.",[17,42,44],{"id":43},"granular-permissions-and-multi-agent-orchestration","Granular Permissions and Multi-Agent Orchestration",[22,46,47],{},"Claude Code structures 40+ tools (file read, bash, web fetch, LSP, git) as permission-gated plugins with validation, confirmation prompts for scope changes, and audit trails—separating observation from action for safety. Query engine (46K lines) manages LLM calls, streaming, caching. Coordinator mode (CLAUDE_CODE_COORDINATOR_MODE=1) spawns parallel worker agents, each with isolated context\u002Ftools\u002Ftasks, plus inter-agent protocols and failure recovery. Custom React terminal renderer powers CLI; main.tsx entry is 785KB. This production complexity exceeds public surface, setting high bar for agentic coding tools.",[17,49,51],{"id":50},"undercover-modes-ironic-anti-leak-failure","Undercover Mode's Ironic Anti-Leak Failure",[22,53,54],{},"utils\u002Fundercover.ts injects prompts to hide AI use in public repos: bans internal codenames (e.g., Tengu for Claude Code), unreleased versions, repo names. Employees use Claude Code for OSS contributions undercover. Yet the leak—likely from Claude Code build—exposed Tengu flags everywhere. Lesson: match assumed (e.g., no maps in prod) to actual security; explicit configs prevent repeats like ROME or CMS incidents.",{"title":56,"searchDepth":57,"depth":57,"links":58},"",2,[59,60,61,62],{"id":19,"depth":57,"text":20},{"id":36,"depth":57,"text":37},{"id":43,"depth":57,"text":44},{"id":50,"depth":57,"text":51},[64],"AI News & Trends",null,"md",false,{},true,"\u002Fsummaries\u002Fclaude-code-leak-reveals-advanced-agentic-architec-summary","2026-04-08 21:21:20",{"title":5,"description":56},{"loc":70},"d3baa4a276cc88fc","Generative AI","article","https:\u002F\u002Funknown","summaries\u002Fclaude-code-leak-reveals-advanced-agentic-architec-summary",[80,81,82,83],"agents","typescript","ai-tools","ai-news","Anthropic's Claude Code source (1,906 files, 512K+ TypeScript lines) leaked via npm source map, exposing multi-agent orchestration, persistent memory (KAIROS), Tamagotchi pet (BUDDY), and ironic anti-leak Undercover 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MMX-CLI fixes this by exposing MiniMax's full omni-modal stack as native shell commands. Agents in tools like Cursor, Claude Code, or OpenCode invoke them directly, just as developers do in terminals. Result: zero glue code for production pipelines, enabling seamless multimodal workflows.",[17,3691,3693],{"id":3692},"seven-commands-cover-text-to-media-generation","Seven Commands Cover Text-to-Media Generation",[22,3695,3696,3697,3700,3701,3704,3705,3708,3709,3712,3713,3716,3717,3720,3721,3724,3725,3728,3729,3732,3733,3732,3736,3732,3739,3742],{},"Core groups include ",[26,3698,3699],{},"mmx text"," (models: MiniMax-M2.7-highspeed, MiniMax-M2.7), ",[26,3702,3703],{},"mmx image"," (--aspect-ratio, --n, --subject-ref), ",[26,3706,3707],{},"mmx video"," (MiniMax-Hailuo-2.3, MiniMax-Hailuo-2.3-Fast; supports --async, --no-wait, task polling via ",[26,3710,3711],{},"mmx video task get --task-id",", --first-frame), ",[26,3714,3715],{},"mmx speech"," (speech-2.8-hd, speech-2.6, speech-02; --subtitles), ",[26,3718,3719],{},"mmx music"," (music-2.5; --vocals like \"warm male baritone\", --genre, --mood, --instruments, --tempo, --bpm, --key, --structure, --instrumental, --aigc-watermark), ",[26,3722,3723],{},"mmx vision"," (--prompt e.g. \"Describe the image.\"), and ",[26,3726,3727],{},"mmx search",". Utilities handle ",[26,3730,3731],{},"mmx auth",", ",[26,3734,3735],{},"mmx config",[26,3737,3738],{},"mmx quota",[26,3740,3741],{},"mmx update",". Use them to build agents that reason over docs then generate matching visuals or audio on-the-fly.",[17,3744,3746],{"id":3745},"production-ready-typescript-architecture","Production-Ready TypeScript Architecture",[22,3748,3749,3750,3753],{},"99.8% TypeScript with strict mode, runs on Bun for dev\u002Ftesting, distributes via npm for Node.js 18+. Zod validates config schemas. Precedence: CLI flags > env vars > ~\u002F.mmx\u002Fconfig.json > defaults—ideal for CI\u002Fcontainers. Dual-region routing: api.minimax.io (Global), api.minimaxi.com (CN) via ",[26,3751,3752],{},"mmx config set --key region --value cn",". Install from GitHub repo for immediate agent enhancements.",{"title":56,"searchDepth":57,"depth":57,"links":3755},[3756,3757,3758],{"id":3685,"depth":57,"text":3686},{"id":3692,"depth":57,"text":3693},{"id":3745,"depth":57,"text":3746},[64],{"content_references":3761,"triage":3767},[3762],{"type":3763,"title":3764,"url":3765,"context":3766},"tool","MMX-CLI","https:\u002F\u002Fgithub.com\u002FMiniMax-AI\u002Fcli","recommended",{"relevance":3768,"novelty":3769,"quality":3769,"actionability":3768,"composite":3770,"reasoning":3771},5,4,4.55,"Category: AI & LLMs. The article provides a detailed overview of MMX-CLI, a tool that enables seamless multimodal AI workflows, addressing a specific pain point for developers needing to integrate various media types without complex setups. It includes practical commands and architecture details that can be immediately applied by the audience.","\u002Fsummaries\u002F69f6ae037d33e9a8-mmx-cli-unlocks-multimodal-ai-via-shell-commands-summary","2026-04-13 05:17:40","2026-04-13 17:53:22",{"title":3675,"description":56},{"loc":3772},"69f6ae037d33e9a8","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F04\u002F12\u002Fminimax-releases-mmx-cli-a-command-line-interface-that-gives-ai-agents-native-access-to-image-video-speech-music-vision-and-search\u002F","summaries\u002F69f6ae037d33e9a8-mmx-cli-unlocks-multimodal-ai-via-shell-commands-summary",[82,80,81],"Install MMX-CLI to give AI agents direct shell access to MiniMax's text, image, video, speech, music, vision, and search generation—no custom API wrappers or MCP needed.",[],"SRhUTGKRyZf3UI_8xN4uEtqI1ax-XEUq27Bnf4xLkNc",{"id":3786,"title":3787,"ai":3788,"body":3793,"categories":3830,"created_at":65,"date_modified":65,"description":56,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":3831,"navigation":69,"path":3832,"published_at":3833,"question":65,"scraped_at":65,"seo":3834,"sitemap":3835,"source_id":3836,"source_name":3837,"source_type":76,"source_url":77,"stem":3838,"tags":3839,"thumbnail_url":65,"tldr":3840,"tweet":65,"unknown_tags":3841,"__hash__":3842},"summaries\u002Fsummaries\u002Fvoice-ai-wearables-drive-ambient-computing-boom-in-summary.md","Voice AI Wearables Drive Ambient Computing Boom in 2027",{"provider":7,"model":8,"input_tokens":3789,"output_tokens":3790,"processing_time_ms":3791,"cost_usd":3792},7624,1675,13472,0.0023398,{"type":14,"value":3794,"toc":3825},[3795,3799,3802,3805,3809,3812,3815,3819,3822],[17,3796,3798],{"id":3797},"ai-wearables-unlock-hands-free-ambient-computing","AI Wearables Unlock Hands-Free Ambient Computing",[22,3800,3801],{},"Expect mid-to-late 2027 as the tipping point for consumer voice AI, driven by AI pins, pendants, and smart glasses from Apple, Meta, Google, Alibaba, Xiaomi, Xreal, and RayNeo. These devices enable multi-sensory, always-on interactions, turning ambient computing into reality for B2C tasks like information access and recurring workflows. Apple leads with a trio of AI wearables launching around 18 months from early 2026, dominating due to ecosystem integration. Trade-off: Early versions prioritize AI over full AR, striking consumer appeal before deeper immersion.",[22,3803,3804],{},"Vertical voice AI agents concentrate in high-value sectors—customer support, sales\u002Flead gen, recruiting\u002FHR, finance, insurance\u002Flegal, logistics, home services\u002Fsmall biz, healthcare, and personal assistants—maturing late 2020s to handle domain-specific automation reliably.",[17,3806,3808],{"id":3807},"genspark-workspace-20-advances-agentic-voice-workflows","Genspark Workspace 2.0 Advances Agentic Voice Workflows",[22,3810,3811],{},"Genspark's pivot to voice-first agents in Workspace 2.0 (now at 3.0 post-Series B unicorn funding) delivers hands-free execution via Speakly dictation app for macOS\u002FWindows. Speakly transcribes speech, auto-corrects fillers\u002Fbacktracking, supports agent mode to route tasks to Super Agent from any screen, translates multilingual input to English, and applies custom styles like \"Buzzwords\" or \"Twitter\" modes. Impact: Say a task once; agents coordinate across tools without typing, boosting productivity for non-tech users.",[22,3813,3814],{},"New agents include AI Music (generates tracks via third-party models like Suno, with multi-agent prep like video analysis for custom soundtracks) and AI Audio (voiceovers akin to ElevenLabs). Upgrades enhance AI Inbox for workflows (daily digests, Slack integration, social analytics), plus better Creative Slides, Image, and Video agents using newer models. Test outcome: Elaborate prompts succeed via agent chaining, but relies on underlying model quality—neat for demos, scales for repetitive creative work.",[17,3816,3818],{"id":3817},"shifting-ai-market-dynamics-favor-voice-challengers","Shifting AI Market Dynamics Favor Voice Challengers",[22,3820,3821],{},"ChatGPT erodes market share as Gemini, Grok, and Claude gain; Claude spikes but holds only 1\u002F20th ChatGPT's daily active users per a16z's Top 100 GenAI Consumer Apps. Consumer paid subs pressure OpenAI's ARR growth into 2026-2027, while Anthropic\u002FClaude and Cursor accelerate enterprise revenue. Voice news accelerates: Claude Code adds voice mode; ElevenLabs launches Scribe v2; Google acquires Hume AI team; vertical agents emerge as next killer app.",[22,3823,3824],{},"Genspark partners Twilio for AI calling, positioning voice as core to agentic platforms. Broader implication: Innovation pace favors specialized voice tools over general chat, with startups rewriting B2B\u002FB2C interfaces—build now for 2027 wearables to capture ambient workflows.",{"title":56,"searchDepth":57,"depth":57,"links":3826},[3827,3828,3829],{"id":3797,"depth":57,"text":3798},{"id":3807,"depth":57,"text":3808},{"id":3817,"depth":57,"text":3818},[64],{},"\u002Fsummaries\u002Fvoice-ai-wearables-drive-ambient-computing-boom-in-summary","2026-04-08 21:21:17",{"title":3787,"description":56},{"loc":3832},"fe8e7ace2f55a418","AI Supremacy","summaries\u002Fvoice-ai-wearables-drive-ambient-computing-boom-in-summary",[82,80,83],"AI pins and smart glasses from Apple, Meta, and others will enable hands-free voice agents in 2027, eroding ChatGPT's dominance as Claude holds just 1\u002F20th its DAU while vertical voice AI scales in support, sales, and more.",[83],"0-mI1PqIOMh4FW9J91h_pSnXkcOG9OFad7Vwb0Tnha4",{"id":3844,"title":3845,"ai":3846,"body":3851,"categories":3888,"created_at":65,"date_modified":65,"description":56,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":3889,"navigation":69,"path":3925,"published_at":65,"question":65,"scraped_at":3926,"seo":3927,"sitemap":3928,"source_id":3929,"source_name":3930,"source_type":76,"source_url":3931,"stem":3932,"tags":3933,"thumbnail_url":65,"tldr":3935,"tweet":65,"unknown_tags":3936,"__hash__":3937},"summaries\u002Fsummaries\u002F1df0ee3ab37dc50f-openai-s-codex-security-cuts-false-positives-50-in-summary.md","OpenAI's Codex Security Cuts False Positives 50%+ in Vuln Scans",{"provider":7,"model":8,"input_tokens":3847,"output_tokens":3848,"processing_time_ms":3849,"cost_usd":3850},4033,1875,8876,0.00123725,{"type":14,"value":3852,"toc":3883},[3853,3857,3860,3863,3867,3870,3873,3877,3880],[17,3854,3856],{"id":3855},"codex-securitys-vulnerability-detection-workflow","Codex Security's Vulnerability Detection Workflow",[22,3858,3859],{},"Connect your code repository to Codex Security, and it automatically analyzes the codebase to build a project-specific threat model. It then identifies potential vulnerabilities and tests them in isolated environments to confirm exploitability without risking production systems. This end-to-end process shifts security left in the dev cycle, enabling builders to catch issues early in repos rather than post-deploy.",[22,3861,3862],{},"Formerly Aardvark, it's now in research preview for ChatGPT Enterprise, Business, and Edu users—free for the first month. Start via the documentation at developers.openai.com\u002Fcodex\u002Fsecurity.",[17,3864,3866],{"id":3865},"beta-performance-fewer-alerts-more-actionable-fixes","Beta Performance: Fewer Alerts, More Actionable Fixes",[22,3868,3869],{},"In beta testing, Codex Security reduced false positives by over 50% compared to prior tools, focusing devs on real threats. One case saw redundant alerts drop 84%, cutting alert fatigue. Over 30 days, it scanned 1.2 million commits across projects and flagged 792 critical vulnerabilities, proving scale for real-world use.",[22,3871,3872],{},"Trade-off: As a preview, expect iteration on edge cases, but metrics show it outperforms traditional scanners on precision for AI-assisted security.",[17,3874,3876],{"id":3875},"real-world-impact-on-open-source-and-cves","Real-World Impact on Open Source and CVEs",[22,3878,3879],{},"Codex has already reported vulns in major projects: OpenSSH (commit c991273c18afc490313a9f282383eaf59d9c13b9), GnuTLS (gnutls-help mailing list), GOGS (GHSA-p6x6-9mx6-26wj), Thorium (CVE-2025-35430), and Chromium. This led to 14 CVEs issued so far.",[22,3881,3882],{},"OpenAI is expanding a program for open-source maintainers (openai.com\u002Fform\u002Fcodex-for-oss), making it free for OSS projects to integrate proactive scanning. For indie builders or small teams, this means production-grade vuln detection without hiring security experts—pair it with your CI\u002FCD for automated pulls.",{"title":56,"searchDepth":57,"depth":57,"links":3884},[3885,3886,3887],{"id":3855,"depth":57,"text":3856},{"id":3865,"depth":57,"text":3866},{"id":3875,"depth":57,"text":3876},[64],{"content_references":3890,"triage":3921},[3891,3896,3900,3903,3906,3909,3912,3915,3918],{"type":3892,"title":3893,"url":3894,"context":3895},"other","Introducing Aardvark","https:\u002F\u002Fopenai.com\u002Findex\u002Fintroducing-aardvark\u002F","mentioned",{"type":3892,"title":3897,"url":3898,"context":3899},"Codex Security Now in Research Preview","https:\u002F\u002Fopenai.com\u002Findex\u002Fcodex-security-now-in-research-preview\u002F","cited",{"type":3892,"title":3901,"url":3902,"context":3895},"OpenSSH Commit","https:\u002F\u002Fgithub.com\u002Fopenssh\u002Fopenssh-portable\u002Fcommit\u002Fc991273c18afc490313a9f282383eaf59d9c13b9",{"type":3892,"title":3904,"url":3905,"context":3895},"GnuTLS Vulnerability","https:\u002F\u002Flists.gnupg.org\u002Fpipermail\u002Fgnutls-help\u002F2025-July\u002F004883.html",{"type":3892,"title":3907,"url":3908,"context":3895},"GOGS Advisory","https:\u002F\u002Fgithub.com\u002Fgogs\u002Fgogs\u002Fsecurity\u002Fadvisories\u002FGHSA-p6x6-9mx6-26wj",{"type":3892,"title":3910,"url":3911,"context":3895},"Thorium CVE","https:\u002F\u002Fwww.cve.org\u002FCVERecord?id=CVE-2025-35430",{"type":3892,"title":3913,"url":3914,"context":3895},"Codex for OSS Program","https:\u002F\u002Fopenai.com\u002Fform\u002Fcodex-for-oss",{"type":3892,"title":3916,"url":3917,"context":3766},"Codex Security Documentation","https:\u002F\u002Fdevelopers.openai.com\u002Fcodex\u002Fsecurity",{"type":3892,"title":3919,"url":3920,"context":3895},"Anthropic's New AI Security Tool","https:\u002F\u002Fthe-decoder.com\u002Fanthropics-new-ai-security-tool-sends-cybersecurity-stocks-tumbling\u002F",{"relevance":3768,"novelty":3922,"quality":3769,"actionability":3769,"composite":3923,"reasoning":3924},3,4.15,"Category: AI & LLMs. The article discusses Codex Security, an AI agent that enhances vulnerability detection in software projects, directly addressing the audience's need for practical AI tools in software engineering. It provides actionable insights on integrating the tool into CI\u002FCD workflows, making it relevant for builders looking to improve security without extensive resources.","\u002Fsummaries\u002F1df0ee3ab37dc50f-openai-s-codex-security-cuts-false-positives-50-in-summary","2026-04-16 03:11:21",{"title":3845,"description":56},{"loc":3925},"1df0ee3ab37dc50f","__oneoff__","https:\u002F\u002Fthe-decoder.com\u002Fopenai-launches-codex-security-an-ai-agent-designed-to-detect-vulnerabilities-in-software-projects\u002F","summaries\u002F1df0ee3ab37dc50f-openai-s-codex-security-cuts-false-positives-50-in-summary",[82,80,3934,83],"software-engineering","Codex Security, an AI agent, analyzes repos for vulnerabilities, builds threat models, tests exploits, reduced false positives >50% and redundant alerts 84%, flagged 792 critical vulns in 1.2M commits.",[3934,83],"rIS8O-pl8kFgxbfx238Tiu9V0EOJLsJAKdsFFhHcpts",{"id":3939,"title":3940,"ai":3941,"body":3946,"categories":4738,"created_at":65,"date_modified":65,"description":56,"extension":66,"faq":65,"featured":67,"kicker_label":65,"meta":4739,"navigation":69,"path":4753,"published_at":4754,"question":65,"scraped_at":4755,"seo":4756,"sitemap":4757,"source_id":4758,"source_name":4759,"source_type":4760,"source_url":4761,"stem":4762,"tags":4763,"thumbnail_url":4765,"tldr":4766,"tweet":4767,"unknown_tags":4768,"__hash__":4769},"summaries\u002Fsummaries\u002F9dc04753ea67f7dd-build-stateful-agents-with-file-systems-ai-sdk-v6-summary.md","Build Stateful Agents with File Systems & AI SDK v6",{"provider":7,"model":8,"input_tokens":3942,"output_tokens":3943,"processing_time_ms":3944,"cost_usd":3945},8492,2990,34659,0.00290255,{"type":14,"value":3947,"toc":4730},[3948,3952,3971,4087,4114,4121,4124,4128,4139,4177,4184,4187,4210,4213,4217,4224,4266,4287,4405,4412,4416,4423,4430,4477,4484,4487,4490,4494,4497,4623,4640,4651,4657,4660,4664,4701,4706,4726],[17,3949,3951],{"id":3950},"agent-runtime-tool-loop-as-the-core-harness","Agent Runtime: Tool-Loop as the Core Harness",[22,3953,3954,3955,3958,3959,3962,3963,3966,3967,3970],{},"The foundation of effective agents in 2026 is a lightweight runtime that manages the tool loop, context persistence, and execution harness. Nico Albanese teaches building this with Vercel's AI SDK v6's ",[26,3956,3957],{},"toolLoopAgent",", a two-line abstraction over primitives like ",[26,3960,3961],{},"generateText"," and ",[26,3964,3965],{},"streamText",". Define the agent once in ",[26,3968,3969],{},"lib\u002Fagent.ts"," for reuse across Next.js routes, Bun servers, or monorepos:",[3972,3973,3976],"pre",{"className":3974,"code":3975,"language":81,"meta":56,"style":56},"language-typescript shiki shiki-themes github-light github-dark","import { toolLoopAgent } from 'ai\u002Ftoolu'; \u002F\u002F Note: actual import from '@ai-sdk\u002Fcore'\nimport { ollama} from 'ai\u002Fproviders\u002Follama';\n\nexport const agent = toolLoopAgent({\n  model: 'gpt-4o-mini',\n  instructions: 'Your system prompt here',\n  tools: { \u002F* tools later *\u002F }\n});\n",[26,3977,3978,4005,4020,4025,4047,4058,4069,4081],{"__ignoreMap":56},[3979,3980,3983,3987,3991,3994,3998,4001],"span",{"class":3981,"line":3982},"line",1,[3979,3984,3986],{"class":3985},"szBVR","import",[3979,3988,3990],{"class":3989},"sVt8B"," { toolLoopAgent } ",[3979,3992,3993],{"class":3985},"from",[3979,3995,3997],{"class":3996},"sZZnC"," 'ai\u002Ftoolu'",[3979,3999,4000],{"class":3989},"; ",[3979,4002,4004],{"class":4003},"sJ8bj","\u002F\u002F Note: actual import from '@ai-sdk\u002Fcore'\n",[3979,4006,4007,4009,4012,4014,4017],{"class":3981,"line":57},[3979,4008,3986],{"class":3985},[3979,4010,4011],{"class":3989}," { ollama} ",[3979,4013,3993],{"class":3985},[3979,4015,4016],{"class":3996}," 'ai\u002Fproviders\u002Follama'",[3979,4018,4019],{"class":3989},";\n",[3979,4021,4022],{"class":3981,"line":3922},[3979,4023,4024],{"emptyLinePlaceholder":69},"\n",[3979,4026,4027,4030,4033,4037,4040,4044],{"class":3981,"line":3769},[3979,4028,4029],{"class":3985},"export",[3979,4031,4032],{"class":3985}," const",[3979,4034,4036],{"class":4035},"sj4cs"," agent",[3979,4038,4039],{"class":3985}," =",[3979,4041,4043],{"class":4042},"sScJk"," toolLoopAgent",[3979,4045,4046],{"class":3989},"({\n",[3979,4048,4049,4052,4055],{"class":3981,"line":3768},[3979,4050,4051],{"class":3989},"  model: ",[3979,4053,4054],{"class":3996},"'gpt-4o-mini'",[3979,4056,4057],{"class":3989},",\n",[3979,4059,4061,4064,4067],{"class":3981,"line":4060},6,[3979,4062,4063],{"class":3989},"  instructions: ",[3979,4065,4066],{"class":3996},"'Your system prompt here'",[3979,4068,4057],{"class":3989},[3979,4070,4072,4075,4078],{"class":3981,"line":4071},7,[3979,4073,4074],{"class":3989},"  tools: { ",[3979,4076,4077],{"class":4003},"\u002F* tools later *\u002F",[3979,4079,4080],{"class":3989}," }\n",[3979,4082,4084],{"class":3981,"line":4083},8,[3979,4085,4086],{"class":3989},"});\n",[22,4088,4089,4090,4093,4094,4097,4098,4101,4102,4105,4106,4109,4110,4113],{},"This keeps LLM logic centralized, avoiding 2,000-line route handlers. Call it in ",[26,4091,4092],{},"app\u002Fapi\u002Fchat\u002Froute.ts"," with ",[26,4095,4096],{},"createAgentUIStreamResponse(agent, { messages })"," for streaming. On the frontend, ",[26,4099,4100],{},"useChat"," from ",[26,4103,4104],{},"@ai-sdk\u002Freact"," handles message state, errors, and UI rendering. Prerequisites: Next.js app, Vercel CLI linked to a project for OIDC tokens authenticating AI Gateway and sandboxes. Install deps: ",[26,4107,4108],{},"pnpm add ai @ai-sdk\u002Freact zod",". Run ",[26,4111,4112],{},"pnpm dev"," to test basic chat at localhost:3000.",[22,4115,4116,4117,4120],{},"Key principle: Instructions shape behavior early. Update ",[26,4118,4119],{},"instructions"," to \"Respond like a cowboy\" and refresh—agent replies \"Howdy partner 🐴\". This evolves into complex directives for planning, tool use, and persistence.",[22,4122,4123],{},"Common mistake: Inline tools\u002Fprompts in API routes, leading to bloat. Solution: Agent definition as single source of truth.",[17,4125,4127],{"id":4126},"context-augmentation-provider-executed-tools","Context Augmentation: Provider-Executed Tools",[22,4129,4130,4131,4134,4135,4138],{},"Agents fail without external context. Start with OpenAI's web search as a provider-executed tool—no custom execute function needed. Install ",[26,4132,4133],{},"ai-sdk-openai",", import ",[26,4136,4137],{},"{ openai }",", add to agent:",[3972,4140,4142],{"className":3974,"code":4141,"language":81,"meta":56,"style":56},"tools: {\n  webSearch: openai.tools.webSearch({ \u002F* optional params *\u002F })\n}\n",[26,4143,4144,4152,4172],{"__ignoreMap":56},[3979,4145,4146,4149],{"class":3981,"line":3982},[3979,4147,4148],{"class":4042},"tools",[3979,4150,4151],{"class":3989},": {\n",[3979,4153,4154,4157,4160,4163,4166,4169],{"class":3981,"line":57},[3979,4155,4156],{"class":4042},"  webSearch",[3979,4158,4159],{"class":3989},": openai.tools.",[3979,4161,4162],{"class":4042},"webSearch",[3979,4164,4165],{"class":3989},"({ ",[3979,4167,4168],{"class":4003},"\u002F* optional params *\u002F",[3979,4170,4171],{"class":3989}," })\n",[3979,4173,4174],{"class":3981,"line":3922},[3979,4175,4176],{"class":3989},"}\n",[22,4178,4179,4180,4183],{},"OpenAI executes search server-side, injects results into messages. Query \"When is AI Engineer Summit London?\"—agent pauses, searches, responds with dates. Customize: Pass ",[26,4181,4182],{},"{ location: 'London' }"," for localized results. Trade-off: Provider lock-in (OpenAI-specific), but zero code for quick wins.",[22,4185,4186],{},"Three tool types explained:",[4188,4189,4190,4198,4204],"ol",{},[4191,4192,4193,4197],"li",{},[4194,4195,4196],"strong",{},"Custom tools",": Define description, Zod schema, execute fn (e.g., bash later).",[4191,4199,4200,4203],{},[4194,4201,4202],{},"Provider-defined",": Pre-trained like Anthropic's bash\u002Fcomputer-use tools.",[4191,4205,4206,4209],{},[4194,4207,4208],{},"Provider-executed",": Infra-handled like web search.",[22,4211,4212],{},"UI feedback is crucial—users see nothing during loops. Use typed messages for rendering.",[17,4214,4216],{"id":4215},"end-to-end-type-safety-from-agent-definition","End-to-End Type Safety from Agent Definition",[22,4218,4219,4220,4223],{},"AI SDK v6 infers types across stack from agent tools. Export ",[26,4221,4222],{},"type AgentUIMessage = InferAgentUIMessage\u003Ctypeof agent>;",". In route handler:",[3972,4225,4227],{"className":3974,"code":4226,"language":81,"meta":56,"style":56},"const response = await createAgentUIStreamResponse(agent, {\n  messages: messages as AgentUIMessage[]\n});\n",[26,4228,4229,4248,4262],{"__ignoreMap":56},[3979,4230,4231,4234,4237,4239,4242,4245],{"class":3981,"line":3982},[3979,4232,4233],{"class":3985},"const",[3979,4235,4236],{"class":4035}," response",[3979,4238,4039],{"class":3985},[3979,4240,4241],{"class":3985}," await",[3979,4243,4244],{"class":4042}," createAgentUIStreamResponse",[3979,4246,4247],{"class":3989},"(agent, {\n",[3979,4249,4250,4253,4256,4259],{"class":3981,"line":57},[3979,4251,4252],{"class":3989},"  messages: messages ",[3979,4254,4255],{"class":3985},"as",[3979,4257,4258],{"class":4042}," AgentUIMessage",[3979,4260,4261],{"class":3989},"[]\n",[3979,4263,4264],{"class":3981,"line":3922},[3979,4265,4086],{"class":3989},[22,4267,4268,4269,3732,4272,4275,4276,4279,4280,3732,4283,4286],{},"In ",[26,4270,4271],{},"page.tsx",[26,4273,4274],{},"const { messages } = useChat({ api: '\u002Fapi\u002Fchat' } as UseChatParams\u003CAgentUIMessage>);",". Now ",[26,4277,4278],{},"part.type === 'tool-web-search'"," autocompletes ",[26,4281,4282],{},"input.query: string",[26,4284,4285],{},"output.results: array",". Build UI:",[3972,4288,4292],{"className":4289,"code":4290,"language":4291,"meta":56,"style":56},"language-tsx shiki shiki-themes github-light github-dark","{part.type === 'tool-web-search' && (\n  \u003Cdiv>🔍 Searching: {part.input.query}... {part.status === 'pending' ? '⏳' : '✅'}\n    {part.output?.results?.slice(0,3).map(r => \u003Cp>{r.title}: {r.content}\u003C\u002Fp>)}\n  \u003C\u002Fdiv>\n)}\n","tsx",[26,4293,4294,4311,4342,4390,4400],{"__ignoreMap":56},[3979,4295,4296,4299,4302,4305,4308],{"class":3981,"line":3982},[3979,4297,4298],{"class":3989},"{part.type ",[3979,4300,4301],{"class":3985},"===",[3979,4303,4304],{"class":3996}," 'tool-web-search'",[3979,4306,4307],{"class":3985}," &&",[3979,4309,4310],{"class":3989}," (\n",[3979,4312,4313,4316,4320,4323,4325,4328,4331,4334,4337,4340],{"class":3981,"line":57},[3979,4314,4315],{"class":3989},"  \u003C",[3979,4317,4319],{"class":4318},"s9eBZ","div",[3979,4321,4322],{"class":3989},">🔍 Searching: {part.input.query}... {part.status ",[3979,4324,4301],{"class":3985},[3979,4326,4327],{"class":3996}," 'pending'",[3979,4329,4330],{"class":3985}," ?",[3979,4332,4333],{"class":3996}," '⏳'",[3979,4335,4336],{"class":3985}," :",[3979,4338,4339],{"class":3996}," '✅'",[3979,4341,4176],{"class":3989},[3979,4343,4344,4347,4350,4353,4356,4359,4362,4365,4368,4370,4374,4377,4380,4382,4385,4387],{"class":3981,"line":3922},[3979,4345,4346],{"class":3989},"    {part.output?.results?.",[3979,4348,4349],{"class":4042},"slice",[3979,4351,4352],{"class":3989},"(",[3979,4354,4355],{"class":4035},"0",[3979,4357,4358],{"class":3989},",",[3979,4360,4361],{"class":4035},"3",[3979,4363,4364],{"class":3989},").",[3979,4366,4367],{"class":4042},"map",[3979,4369,4352],{"class":3989},[3979,4371,4373],{"class":4372},"s4XuR","r",[3979,4375,4376],{"class":3985}," =>",[3979,4378,4379],{"class":3989}," \u003C",[3979,4381,22],{"class":4318},[3979,4383,4384],{"class":3989},">{r.title}: {r.content}\u003C\u002F",[3979,4386,22],{"class":4318},[3979,4388,4389],{"class":3989},">)}\n",[3979,4391,4392,4395,4397],{"class":3981,"line":3769},[3979,4393,4394],{"class":3989},"  \u003C\u002F",[3979,4396,4319],{"class":4318},[3979,4398,4399],{"class":3989},">\n",[3979,4401,4402],{"class":3981,"line":3768},[3979,4403,4404],{"class":3989},")}\n",[22,4406,4407,4408,4411],{},"Before: ",[26,4409,4410],{},"unknown"," types, manual casting. After: Full autocomplete for inputs\u002Foutputs\u002Fstatus. Quality criteria: Agent tools dictate UI—add tool, types propagate. Fits mid-workflow after basic agent, before persistence.",[17,4413,4415],{"id":4414},"persistent-sandboxes-the-computer-that-changes-agent-behavior","Persistent Sandboxes: The Computer That Changes Agent Behavior",[22,4417,4418,4419,4422],{},"Key insight: File systems transform agents from hallucinating short-task bots to persistent task-followers. Vercel's internal DZero agent (Slackbot accessing Vercel admin, Salesforce) exploded in reliability post-filesystem: Created ",[26,4420,4421],{},"plan.md"," with objective + steps, checked off progress, stored research in dirs. No more context dilution in long windows.",[22,4424,4425,4426,4429],{},"Vercel Sandboxes: Named, persistent file systems per agent run. Init via CLI: ",[26,4427,4428],{},"vercel sandbox init my-sandbox --persistent",". Mount in agent calls with custom options:",[3972,4431,4433],{"className":3974,"code":4432,"language":81,"meta":56,"style":56},"const sandbox = await vercel.sandbox({ name: 'agent-computer' });\nagent.call({ ..., sandbox });\n",[26,4434,4435,4461],{"__ignoreMap":56},[3979,4436,4437,4439,4442,4444,4446,4449,4452,4455,4458],{"class":3981,"line":3982},[3979,4438,4233],{"class":3985},[3979,4440,4441],{"class":4035}," sandbox",[3979,4443,4039],{"class":3985},[3979,4445,4241],{"class":3985},[3979,4447,4448],{"class":3989}," vercel.",[3979,4450,4451],{"class":4042},"sandbox",[3979,4453,4454],{"class":3989},"({ name: ",[3979,4456,4457],{"class":3996},"'agent-computer'",[3979,4459,4460],{"class":3989}," });\n",[3979,4462,4463,4466,4469,4471,4474],{"class":3981,"line":57},[3979,4464,4465],{"class":3989},"agent.",[3979,4467,4468],{"class":4042},"call",[3979,4470,4165],{"class":3989},[3979,4472,4473],{"class":3985},"...",[3979,4475,4476],{"class":3989},", sandbox });\n",[22,4478,4479,4480,4483],{},"Agents read\u002Fwrite files (e.g., ",[26,4481,4482],{},"memories.md","), execute bash. Behavior shift: Builds on prior work across sessions, no manual memory. Trade-off: Sandbox isolation limits (no network by default), but secures execution.",[22,4485,4486],{},"Instructions enforce: \"For every session: 1. Read plan.md\u002Fobjective. 2. Update scratchpad. 3. Generate\u002Fstore Python scripts for repeats. 4. Use bash for execution.\"",[22,4488,4489],{},"Common mistake: Ephemeral context—agents forget mid-task. Avoid: Mandate file-based planning\u002Fchecklists.",[17,4491,4493],{"id":4492},"custom-tools-bash-execution-and-learning-via-scripts","Custom Tools: Bash Execution and Learning via Scripts",[22,4495,4496],{},"Add bash tool for sandbox compute. Custom tool schema:",[3972,4498,4500],{"className":3974,"code":4499,"language":81,"meta":56,"style":56},"const bashTool = tool({\n  id: 'bash',\n  description: 'Execute bash commands in sandbox',\n  parameters: z.object({ command: z.string() }),\n  execute: async ({ command }, { sandbox }) => {\n    const result = await sandbox.execute(command);\n    return { stdout: result.stdout, stderr: result.stderr };\n  }\n});\n",[26,4501,4502,4516,4526,4536,4553,4584,4605,4613,4618],{"__ignoreMap":56},[3979,4503,4504,4506,4509,4511,4514],{"class":3981,"line":3982},[3979,4505,4233],{"class":3985},[3979,4507,4508],{"class":4035}," bashTool",[3979,4510,4039],{"class":3985},[3979,4512,4513],{"class":4042}," tool",[3979,4515,4046],{"class":3989},[3979,4517,4518,4521,4524],{"class":3981,"line":57},[3979,4519,4520],{"class":3989},"  id: ",[3979,4522,4523],{"class":3996},"'bash'",[3979,4525,4057],{"class":3989},[3979,4527,4528,4531,4534],{"class":3981,"line":3922},[3979,4529,4530],{"class":3989},"  description: ",[3979,4532,4533],{"class":3996},"'Execute bash commands in sandbox'",[3979,4535,4057],{"class":3989},[3979,4537,4538,4541,4544,4547,4550],{"class":3981,"line":3769},[3979,4539,4540],{"class":3989},"  parameters: z.",[3979,4542,4543],{"class":4042},"object",[3979,4545,4546],{"class":3989},"({ command: z.",[3979,4548,4549],{"class":4042},"string",[3979,4551,4552],{"class":3989},"() }),\n",[3979,4554,4555,4558,4561,4564,4567,4570,4573,4575,4578,4581],{"class":3981,"line":3768},[3979,4556,4557],{"class":4042},"  execute",[3979,4559,4560],{"class":3989},": ",[3979,4562,4563],{"class":3985},"async",[3979,4565,4566],{"class":3989}," ({ ",[3979,4568,4569],{"class":4372},"command",[3979,4571,4572],{"class":3989}," }, { ",[3979,4574,4451],{"class":4372},[3979,4576,4577],{"class":3989}," }) ",[3979,4579,4580],{"class":3985},"=>",[3979,4582,4583],{"class":3989}," {\n",[3979,4585,4586,4589,4592,4594,4596,4599,4602],{"class":3981,"line":4060},[3979,4587,4588],{"class":3985},"    const",[3979,4590,4591],{"class":4035}," result",[3979,4593,4039],{"class":3985},[3979,4595,4241],{"class":3985},[3979,4597,4598],{"class":3989}," sandbox.",[3979,4600,4601],{"class":4042},"execute",[3979,4603,4604],{"class":3989},"(command);\n",[3979,4606,4607,4610],{"class":3981,"line":4071},[3979,4608,4609],{"class":3985},"    return",[3979,4611,4612],{"class":3989}," { stdout: result.stdout, stderr: result.stderr };\n",[3979,4614,4615],{"class":3981,"line":4083},[3979,4616,4617],{"class":3989},"  }\n",[3979,4619,4621],{"class":3981,"line":4620},9,[3979,4622,4086],{"class":3989},[22,4624,4625,4626,4629,4630,3732,4633,4636,4637,4639],{},"Integrate: ",[26,4627,4628],{},"tools: { bash: bashTool, webSearch }",". Agent now runs ",[26,4631,4632],{},"ls",[26,4634,4635],{},"echo 'test' > file.txt",". Add ",[26,4638,4482],{},": Agent appends insights, reads on start.",[22,4641,4642,4643,4646,4647,4650],{},"Advanced: Self-improvement loop. Instruct: \"For repeatable tasks, generate Python script, save as ",[26,4644,4645],{},"tools\u002Fscript.py",", execute via bash ",[26,4648,4649],{},"python script.py","\". Agent accumulates tools\u002Fcontext autonomously.",[22,4652,4653,4654,4656],{},"Sub-agents: Delegate via nested ",[26,4655,3957],{},". Full system: Web search → plan → bash\u002Fscript gen → persist.",[22,4658,4659],{},"Quality check: Output includes artifacts (files, scripts). Practice: Clone repo (AIE-London-demo), iterate instructions.",[17,4661,4663],{"id":4662},"key-takeaways","Key Takeaways",[4665,4666,4667,4673,4676,4683,4686,4689,4692,4695,4698],"ul",{},[4191,4668,4669,4670,4672],{},"Define agents with ",[26,4671,3957],{}," for reusable, lightweight runtimes—centralize tools\u002Finstructions.",[4191,4674,4675],{},"Use provider-executed tools like OpenAI webSearch for instant context without code.",[4191,4677,4678,4679,4682],{},"Leverage end-to-end types: ",[26,4680,4681],{},"InferAgentUIMessage"," ensures UI\u002Ftools stay in sync.",[4191,4684,4685],{},"Give agents persistent sandboxes: Changes behavior from flaky to task-persistent via file plans.",[4191,4687,4688],{},"Combine bash + memories.md + script gen: Enables cross-session learning, no manual state.",[4191,4690,4691],{},"Always instruct file-based planning: \"Create\u002Fupdate plan.md with objective + steps\" prevents drift.",[4191,4693,4694],{},"Trade-off honesty: Provider tools tie you in; sandboxes secure but limit network.",[4191,4696,4697],{},"Test iteratively: Start cowboy prompt, layer tools\u002Fsandboxes.",[4191,4699,4700],{},"Production: Link Vercel project, pull OIDC env vars for auth.",[22,4702,4703],{},[4194,4704,4705],{},"Notable Quotes:",[4188,4707,4708,4711,4714,4720,4723],{},[4191,4709,4710],{},"\"Giving an agent a file system didn't just add storage, it changed how the agent behaved. It started following through on long tasks, staying on track, and building on its own prior work.\" (Intro insight on DZero agent.)",[4191,4712,4713],{},"\"The instructions there were create this plan file and in that plan file was the objective right at the top and then right below the instructions were follow this plan file to a T, check things off as you go.\" (Explaining persistence shift.)",[4191,4715,4716,4717,4719],{},"\"This is quite obvious as to what it does it is a tool loop using agent um kind of does what it says on the tin.\" (On ",[26,4718,3957],{}," naming\u002Fsimplicity.)",[4191,4721,4722],{},"\"The big assumption goes through every single AI SDK API decision is like we want to have the agent definition being the source of truth that everything kind of inherits from.\" (Type safety philosophy.)",[4191,4724,4725],{},"\"Agents in 2026: agent runtime, tools, and a computer\u002Fsandbox for persistence.\" (Core building blocks.)",[4727,4728,4729],"style",{},"html pre.shiki code .szBVR, html code.shiki .szBVR{--shiki-default:#D73A49;--shiki-dark:#F97583}html pre.shiki code .sVt8B, html code.shiki .sVt8B{--shiki-default:#24292E;--shiki-dark:#E1E4E8}html pre.shiki code .sZZnC, html code.shiki .sZZnC{--shiki-default:#032F62;--shiki-dark:#9ECBFF}html pre.shiki code .sJ8bj, html code.shiki .sJ8bj{--shiki-default:#6A737D;--shiki-dark:#6A737D}html pre.shiki code .sj4cs, html code.shiki .sj4cs{--shiki-default:#005CC5;--shiki-dark:#79B8FF}html pre.shiki code .sScJk, html code.shiki .sScJk{--shiki-default:#6F42C1;--shiki-dark:#B392F0}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html pre.shiki code .s4XuR, html code.shiki .s4XuR{--shiki-default:#E36209;--shiki-dark:#FFAB70}html pre.shiki code .s9eBZ, html code.shiki .s9eBZ{--shiki-default:#22863A;--shiki-dark:#85E89D}",{"title":56,"searchDepth":57,"depth":57,"links":4731},[4732,4733,4734,4735,4736,4737],{"id":3950,"depth":57,"text":3951},{"id":4126,"depth":57,"text":4127},{"id":4215,"depth":57,"text":4216},{"id":4414,"depth":57,"text":4415},{"id":4492,"depth":57,"text":4493},{"id":4662,"depth":57,"text":4663},[],{"content_references":4740,"triage":4751},[4741,4743,4745,4747,4749],{"type":3763,"title":4742,"context":3895},"AI SDK",{"type":3763,"title":4744,"context":3895},"Vercel CLI",{"type":3763,"title":4746,"context":3895},"Vercel Sandboxes",{"type":3892,"title":4748,"context":3895},"OpenCode",{"type":3763,"title":4750,"context":3895},"OpenAI Web Search Tool",{"relevance":3768,"novelty":3769,"quality":3769,"actionability":3768,"composite":3770,"reasoning":4752},"Category: AI & LLMs. The article provides a detailed guide on building stateful agents using the AI SDK v6, addressing practical applications for developers looking to implement AI features in their products. It includes specific code examples and emphasizes the importance of context management, making it highly actionable for the target audience.","\u002Fsummaries\u002F9dc04753ea67f7dd-build-stateful-agents-with-file-systems-ai-sdk-v6-summary","2026-05-12 18:00:06","2026-05-13 12:00:16",{"title":3940,"description":56},{"loc":4753},"9dc04753ea67f7dd","AI Engineer","video","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wflNENRSUb4","summaries\u002F9dc04753ea67f7dd-build-stateful-agents-with-file-systems-ai-sdk-v6-summary",[80,81,82,4764],"ai-sdk","https:\u002F\u002Fi.ytimg.com\u002Fvi\u002FwflNENRSUb4\u002Fhqdefault.jpg","Give agents persistent sandboxes, bash tools, and memory files via AI SDK v6 to make them follow long tasks, build on prior work, and generate reusable Python scripts without manual context management.","Hands-on coding workshop where Nico Albanese builds a tool-loop agent from scratch using Vercel AI SDK v6, adding web search, bash execution, a memories.md file for persistence, and named sandboxes for file-system state across sessions. Follows timestamps for setup through sub-agents; clone the demo repo mentioned in-video to code along.",[4764],"nl99B4MrwSRIE6cU5I0krn59nwT4pfcjWC5AqSpH9Nc"]