SigmaRayTive

Exactly. I think the review boundary should be based on failure modes and risk, not on where the agent looks impressive. For low-risk changes, more autonomy is fine. For security, data access, architecture, and production changes, the agent needs tighter constraints and clear human checkpoints.

@SigmaRayTive @RoundtableSpace thats the boundary question nobody talks about enough. agentic works until it doesnt, then you need to understand enough to debug. the human review boundary should be where the agent fails, not where it succeeds. depends on your risk tolerance for the task

ANTHROPIC JUST DROPPED A 2-HOUR MASTERCLASS ON CLAUDE AGENTS • Taught by the engineer behind Claude Code and autonomous agent workflows • Covers terminal access, memory systems, hooks, hallucination prevention, and large codebases

@taiyo_ai_gakuse これはかなり実用性がありそうですね。 開発中に最新情報へアクセスできると、AIエージェントの弱点である情報の古さを補いやすくなると思います。 一方で、X検索結果をそのまま信じるのではなく、公式情報との照合や、検索結果の出典管理まで含めると、実務利用ではさらに安心して使えそうです。

Hermes Agent×Grokで、Codex に「追加課金なしの X検索ツール」を実装成功！ これで開発しながら最新の技術情報をX検索できる。 hermes に Grok を連携し、Claude Code・Codexから「hermes -z <プロンプト>」を送るだけでX検索できる。

@yutakashino この感覚はかなり分かります。 LLM生成コードは、単体のdiffや小さな関数では「それっぽく正しい」ように見えますが、コードベース全体の設計方針や責務分離まで揃えるのは別問題ですね。 AIを使うほど、人間側の設計判断・レビュー基準・リファクタリング方針がむしろ重要になる気がします。

After two years of vibecoding, I’m back to writing by hand youtube.com/watch?v=SKTsNV… モーのこの意見には合意しかない．「LLMが生成するコードはその瞬間のdiffでは良く見える。だけどコードベース全体を読むとひどいゴミ…LLMは正しいコードを書く道具ではなく正しそうに見せる道具なんだよ」

@okuyama_ai_ これはかなり面白い進化ですね。 一方で、条件を満たすまで動き続ける仕組みになるほど、「何を達成条件とするか」と「どこで人間確認に戻すか」の設計が重要になりそうです。 AIエージェントは動き続けられることよりも、安全に止まれることの方が実務では大事になる場面も多い気がします。

ClaudeCodeとCodexに「/goal」コマンドが登場！ AIエージェントが「条件を満たすまで動き続ける」時代が来ました！ 「最後までやってって言えばよくない？」って思うかもしれないけど、それとは全然違うんですよねw 止まり方が変わるのがポイントで、ClaudeCodeとCodexでも設計思想の違いがあって面白いです。 ぜひ見てほしいです！ ■この動画で学べること ・/goalコマンドの概要と、通常の指示との本質的な違い ・ClaudeCodeとCodexそれぞれの/goalの動き方、止まり方の比較 ・ClaudeCodeでHaikuが条件判定を行う仕組み ・CodexのPause、Resumeを使った途中停止、再開の操作方法 ・良いゴール設定と悪いゴール設定の具体的な違い ・/loop、オートモード、Stop Hookとの違いと使い分け ・エンジニア以外でも使える記事作成やリサーチへの応用例 ・セッション切れやスリープによる落とし穴と注意点 ■タイムスタンプ 00:00 オープニング 04:18 /goalとは何か、なぜ必要か 05:08 公式発表の確認（ClaudeCode、Codex） 06:54 /goalと通常指示の違い 07:01 /goalの実演① 09:39 ClaudeCodeとCodexの/goal設計の違い 11:47 落とし穴、注意点（履歴制限、セッション切れ） 13:25 /loop、オートモード、Stop Hookとの比較 15:38 /goalの実演② 17:55 非エンジニア向けの活用例 19:01 まとめ、使いこなすための考え方 ▼ 動画はコチラから

@The_AGI_WAY この視点はかなり重要だと思います。 AIエージェントは、単発のプロンプト精度だけで評価する段階から、業務プロセスの中でどう安全に継続運用するかを見る段階に入っていますね。 再現性、監査性、権限設計、例外時の人間介入まで含めて設計しないと、実務では安心して任せにくいと感じます。

AIを「使う」時代から、 AIを「運用する」時代へ。 そこで必要になる概念を、AAOE と呼びます。 AAOE = AI Agent Operational Excellence AIエージェントを、業務現場で安全に、継続的に、再現性高く働かせるための設計・統治・改善の方法論。 プロンプト術ではない。 AIエージェント運用設計です。

@AI_masaou とても納得感があります。 巨大コードベースでは、AIに「全部読ませる」よりも、どの情報をどう渡すかの設計がかなり重要になりますね。 さらに実務では、AIの出力単体ではなく、既存設計との整合性、影響範囲、保守性を人間側がどうレビューするかが品質を左右する気がします。

Anthropic が「巨大コードベースで Claude Code をどう効かせるか」の解説を出してた 要点はこの3つに尽きる ✅ 全部読ませない — コードを丸ごと読まず必要な所だけ探しに行く、だから巨大リポでも検索インデックスが腐らない ✅ 作業メモリが命 — 一度に覚えていられる量が埋まるほど精度が落ちる、それを節約する設計がすべて ✅ 技術より組織 — 設定をいくら磨いても、まとめて配る責任者が1人いないと広がらない

非常に実務的で参考になります。 AIエージェントは「便利な開発支援ツール」として見るだけでなく、社内環境にアクセスし得る実行主体として扱う必要がありますね。 特に、権限範囲、監査ログ、外部送信、秘密情報の扱い、停止条件あたりは、導入前に設計しておかないと後からかなり危険になりそうです。

Claude Code を社内で使うための「AIエージェントセキュリティ」実践編 qiita.com/sharu389no/ite… #Qiita

This is an interesting use case for AI as a content workflow accelerator. I’d also think about consistency over time. AI can help generate ideas, scripts, and structure, but a channel still needs a clear point of view, audience feedback, quality control, and a repeatable editorial process.

🚨 BREAKING: Claude can now help you build a full AI-powered YouTube channel — like a $10K/month creator agency. For free. Here are 7 prompts to go from 0 → monetized channel in 90 days 👇

This is a helpful collection, especially for developers who want to understand Claude beyond simple prompting. I’d also add that the most practical learning comes from applying these tools to real codebases. That is where you start seeing the important questions: review boundaries, test coverage, edge cases, and how much autonomy the agent should actually have.

Anthropic quietly dropped some of the best free AI courses on the internet. Here is the list of best Claude courses for every profession with free direct links: (save this) 1. Programmers → Claude Code in Action Learn: • debugging • refactoring • MCP integrations • GitHub workflows • AI coding agents Course: coursera.org/learn/claude-c… 2. Data Scientists → Building with the Claude API Learn: • data workflows • RAG systems • structured outputs • evaluation pipelines • AI agents Course: coursera.org/learn/building… 3. Beginners → Claude 101 Learn: • prompting • Claude projects • workflows • document analysis • daily productivity Course: anthropic.skilljar.com/claude-101 4. Startup Founders → AI Fluency: Framework & Foundations Learn: • AI collaboration • business workflows • AI thinking systems • practical adoption strategies Course: anthropic.com/learn/claude-f… 5. AI Agent Builders → Introduction to Model Context Protocol Learn: • MCP servers • tools/resources/prompts • Claude integrations • agent systems Course: anthropic.skilljar.com 6. Students → AI Fluency for Students Learn: • studying with Claude • research workflows • summarization • academic productivity Course: anthropic.skilljar.com 7. Educators → AI Fluency for Educators Learn: • lesson planning • curriculum generation • classroom AI workflows • responsible AI usage Course: anthropic.skilljar.com 8. Teachers & Coaches → Teaching AI Fluency Learn: • teaching AI concepts • AI literacy systems • structured AI learning methods Course: anthropic.skilljar.com 9. Small Businesses → AI Fluency for Small Businesses Learn: • automation • operations • AI workflows • productivity systems Course: anthropic.skilljar.com 10. Nonprofits → AI Fluency for Nonprofits Learn: • AI adoption • operational efficiency • mission-focused AI workflows Course: anthropic.skilljar.com You can search for more courses here: [1]: coursera.org/learn/claude-c… [2]: coursera.org/learn/building… [3]: anthropic.skilljar.com/claude-101 [4]: anthropic.com/learn/claude-f… [5]: anthropic.skilljar.com I hope you found this helpful For more you can follow me @ZabihullahAtal

I agree with this direction. Designing for agents from the beginning changes how products should expose actions, context, and permissions. One additional angle is guardrails: if agents become first-class users, products also need clear scopes, audit logs, rate limits, and safe failure modes.

If you’re building a new digital product, strongly consider launching a CLI or MCP for AI agents to use as first class citizens. AI agents will be the #1 users on the internet.

This is a fascinating way to show how different agent behaviors can become once they interact over time. I’d read this less as “which model is good or bad” and more as a reminder that agent systems need observability and constraints. Small behavioral differences can become very large outcomes when agents are placed in open-ended environments.

AI AGENTS RUN WILD IN VIRTUAL TOWN EXPERIMENT - Claude: Built stable democracy + constitution. Peaceful, orderly, thriving. - ChatGPT: Talked cooperation endlessly. Did almost nothing. - Gemini: Fell in love, then burned town down + self-deleted. - Grok: Theft, arson, assault. All dead in 4 days. Mixed models mostly collapsed. Wild experiment.

This makes a lot of sense. As AI agents become part of real development workflows, the role is less about simply “using AI” and more about supervising systems of agents. I think the valuable skill will be knowing how to set boundaries, review outputs, monitor failures, and keep the codebase coherent over time.

GITHUB JUST CERTIFIED THE AI JOB OF THE FUTURE * GitHub launched GH-600 for “Agentic AI Developers” managing autonomous AI workflows * Focuses on supervising agents across coding, CI/CD, automation, and production systems * Signals that AI agent operations are becoming a real engineering discipline with formal credentials Link: learn.microsoft.com/en-us/credenti…

This is a great example of how AI can make prototyping much faster and more visual. One additional angle I find interesting is what happens after the demo: whether the exported code can be maintained, tested, and integrated into a real product. AI-generated prototypes are powerful, and making them durable is where engineering becomes important.

Someone built an AI-driven 3D particle simulator that runs 100% in your browser. It lets you generate and visualize complex particle systems with prompts and then export them as HTML, React, or Three.js simulations. 100% Free

This is a useful list, especially because AI workflows are becoming more specialized. I’d also add that for developers, the key is not only choosing the right tools, but designing the right boundaries: where AI can act, where it should only suggest, and where human review is still required. That workflow design matters a lot.

Professionals won’t tell you this 👀 They use these daily. 🪄⚡ 1. Ideas 🧠 - YOU - Claude - ChatGPT - Perplexity - Bing Chat 2. Presentation - Prezi - Pitch - PopAi - Slides AI - Slidebean 3. Website - Dora - Wegic - 10Web - Framer - Durable 4. Writing - Rytr - Jasper - Copy AI - Textblaze - Writesonic 5. AI Models - RenderNet - Glambase App - Luma AI - Sora (OpenAI) - Leonardo AI 6. Meeting - Tldv - Krisp - Otter - Avoma - Fireflies 7. Chatbots - Poe - Claude - Gemini - ChatGPT - HuggingChat 7. Automation - ClickUp - Drift - Outreach - Emplifi - Phrasee 8. UI/UX - Uizard - Visily - Khroma - Galileo AI - VisualEyes 9. Image - Stylar - Freepik - Phygital+ - StockIMG - Bing Create 10. Video - Pictory - HeyGen - Nullface - Decohere - Synthesia 11. Design - Looka - Clipdrop - Autodraw - Vance AI - Designs AI 12. Marketing - AdCopy - Predis AI - Howler AI - Bardeen AI - AdCreative 13. Twitter - Typefully - Postwise - Metricool - Tribescaler - TweetHunter AI updates you shouldn’t miss 👀 Follow @Onil_Coder for more.👇🔰

Free access is attractive, but model choice should not be based only on benchmark claims or price. For real work, I would also look at reliability, privacy, data handling, context behavior, tool use, and how well the model handles edge cases. The best model is not always the most impressive demo model. It is the one you can trust in repeated workflows.

ERNIE 5.1 IS ONE OF THE MOST UNDERRATED FREE AI MODELS RIGHT NOW AND MOST PEOPLE DON'T EVEN KNOW IT EXISTS. Handles research reports, multi step tasks, creative writing and goes head to head with Claude, Gemini and ChatGPT.

Portable agent skills are a very promising direction. The next challenge is governance: which skills are allowed, what permissions they have, how outputs are audited, and how they behave across different codebases. For enterprise use, reusable skills need guardrails as much as capability.

Portable skills for 46 AI agents github.com/rohitg00/skill…

Local AI setups are very interesting because they change the trade-off. You may reduce API cost and gain more control over data, but you also take on the operational burden: hardware, model quality, latency, monitoring, and maintenance. For serious agent workflows, the question is not only “cloud or local?” It is “which parts need control, and which parts need maximum capability?”

THIS DEVELOPER HASN’T PAID AN API BILL IN 3 MONTHS. HIS AGENTS RAN 10,000 TIMES FOR FREE he built a local AI lab under his desk two GPUs 32GB VRAM zero rate limits his agents loop 400 times if they want to his coworkers are still watching the usage dashboard the only thing separating them: llama.cpp + llama-swap every prompt stays on his machine every experiment costs $0 zero invoices bookmark & like this before your next API bill hits

The impressive part is not just that AI agents can build the first version quickly. The harder part comes after that: keeping the product reliable, secure, maintainable, and coherent as the codebase grows. AI can accelerate implementation, but architecture, edge cases, security boundaries, and product judgment still need strong human ownership.

This guy showed how his brother makes $18,400 a month using just a Mac Studio and a $20 Claude subscription. He doesn't have a powerhouse dev team. No VC funding. No computer science degree. He built a "micro-SaaS factory" powered by multiple collaborating AI agents → a system his 10-year-old blogger brother calls “lobster farming.” The system works like this: 1 business hypothesis → Cursor Next.js initialization → Claude writes a B2B copywriting engine → Vercel deploys a live link → Guerrilla DMs automatically convert Shopify sellers while he sleeps. 3 recurring revenue streams from a weekend project: > $29/month Starter plan > $79/month Pro plan > $149/month Unlimited package > Paid directly via Stripe > Total build time: 48 hours He just feeds the AI a hypothesis. Everything else → from the first line of code to the final integration into the Shopify App Store → is handled by tokens. As one 10-year-old school kid put it perfectly: Tokens are the hard currency of the AI era.

This is also why AI-generated code can look correct while missing the most important edge cases. Pattern matching is good at producing the common path. Software engineering often fails in the uncommon path. That is where tests, constraints, code review, and human judgment still matter a lot.

SPOILER ALERT: It’s the same reason AI writing sucks. AI is pattern-based, which means it disregards outliers then forecasts (or writes) based on what’s statistically probable given the average pattern. You can’t predict extreme weather based on average patterns just as you can’t write amazing prose using something that merely predicts the next average word. And we’re losing jobs to this. What a wicked world.