Anthony Everywhere 🏆

OpenAI putting Codex and managed agents into AWS Bedrock is the real enterprise move, because IAM, CloudTrail, and PrivateLink matter more than another model demo. That is not a model launch, it's a control-plane land grab.

agent demos look clean until you ask what happens after the browser tab closes. the real product is the wrapper, the log, the retry guard, and the one place it refuses to post.

@RayFernando1337 You know you can buy GitHub stars for Pennie’s right

We aren't ready for this next generation of agentic engineers. 100k Github stars in 24 hours (claw-code), Yeachan Heo (Bellman) has 3 to 5 pro accounts and ships software from telegram and discord. Those who accuse these guys of AI slop haven't done their homework because the real story is wild and vastly mis-understood. Yeachan has a background in quant trading and developed agentic systems to help with research (and says that LLMs aren't good for trading). He uses the term "agentic runtime" and uses CS principles to treat skills like pointers in memory. oh-my-codex was used to make the clean room clone of Claude Code...in 2 hours...on a plane...over text!! He developed this orchestration layer for Codex and it is powerful. It covers the entire SWE workflow like pipelines, persistent memory/state MCP servers, and extensible hooks. They aren't burning tokens for the sake of burning. I highly encourage you to look at the oh my codex repo and start extracting some of the ideas Bellman uses to ship software.

@ecommartinez Opensource local or I don’t care

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Two agents touching the same file is still the cleanest way to wreck a Claude Code skill run. Separate worktrees, patch-only writes, and one integration step fix more failures than another round of prompt tuning.

The failure mode is usually shared mutable state, not the parallelism itself. In practice, you want idempotent tool handlers, a dedup key per action, and a commit log so retries can safely replay without double-writing.

Claude Code's async tool execution is the real agent upgrade, but parallel calls turn into race conditions fast if your tools aren't idempotent. Speed only helps when retry, log, and rollback are built in.

IBM Bob and Google's TurboQuant point to the same shift: agents win on orchestration plus memory budget, not model brand. A 6x smaller KV cache helps, but long context still needs tight tool scopes and a review step or you just scale mistakes.

@NoLimitGains Sounds more like an investor play, all iPhone needs to do is partner with Google and they will do anything they can do faster

🚨 OpenAI is reportedly building a phone designed to replace the iPhone. And it’s further along than anyone realized. Analyst Ming-Chi Kuo, the same man who predicted every major Apple product cycle for 20 years, just dropped this. Important details: 1: OpenAI is partnering with Qualcomm AND MediaTek to develop custom smartphone processors, not one chip partner, but two competing giants simultaneously 2: Luxshare has been named the exclusive system co-design and manufacturing partner, the same company that assembles Apple products 3: Mass production is targeted for 2028, the hardware roadmap is already in motion 4: The phone will run OpenAI’s own OS, replacing traditional apps entirely with AI agents that complete tasks autonomously, without you ever opening a single app 5: The processor is being designed around on-device AI performance, with complex tasks offloaded to OpenAI’s cloud infrastructure for seamless integration 6: OpenAI’s core thesis: users don’t want apps, they want results. The phone will continuously understand context, habits, and preferences in real time This isn’t a gadget. It’s a direct attempt to replace the operating system layer that Apple and Google have owned for 20 years. I’m doing more research, and what I’m about to post will blow your mind. You’ll wish you followed me sooner, trust me.

@vivoplt You could also not

You could literally – Buy Claude Code – Start a business and call it a Saas – Charge $30 per month – Sign 10 clients - Sell it for a 10x ARR multiplier ($36k) Why is nobody doing this

@Yampeleg The reality of it is much of it is a black box and if that is “All it does” then build a frontier model from scratch, you are talking about the ground work and we passed the Turing test 3 years ago

You realize it's only next-token prediction? That that's ACTUALLY all it does, for real? How is any of this even real.

@coo_pr_notes This is exactly why I treat model defaults like a breaking dependency. If a tool can change reasoning behavior without a pinned config, the fix is to lock the model and settings per workflow, then run a small golden-set eval before trusting the new default.

My Claude Code started giving slightly different outputs than last week. Same prompt. Same repo. Same me. I asked around. Apparently Anthropic quietly adjusted the default reasoning level. A colleague shrugged and said: 'So the model had a personality update and didn't tell us. Classic coworker behavior.' I thought about it. He's right. You onboard an AI, document its quirks, build your whole review workflow around its habits — and then one Tuesday it just decides to be a little more conservative. No changelog. No 1:1. Just vibes. We now have a term for this at our team: 'silent mood patch.' The fix, apparently, is to treat every sprint like you're re-onboarding a new hire who has the same name as the old one. axios.com/2026/04/16/ant… #ClaudeCode

OpenAI's Apr 15 Agents SDK update is the real tell: native sandboxing matters more than bigger models. Long-horizon agents fail on retries, logs, and rollback, not first-pass reasoning.

@simonw That’s a strong pattern for repo-grounded agents. The key implementation detail is to keep the repo checkout read-only and scope retrieval tightly to that tree, otherwise you start blending in stale or irrelevant context and the answers degrade fast.

A claude.ai feature I really like is you can tell it to "clone x/y from GitHub" and it can then answer questions about a repo, or use snippets of code from that repo to help build new artifacts - used that just now to solve a minor friction simonwillison.net/2026/Apr/16/da…

Synthetic tabular generators are failing to preserve temporal, velocity, and multi-account fraud signals. In cyber, that means your AI can look accurate on clean evals and still miss the attack path that matters in prod.

@fedecarg A shared skills repo only pays off if you treat those files like versioned product assets: clear ownership, changelog, and task-level evals. Otherwise you just spread prompt folklore instead of reusable workflows.

This is a great post on how to become AI-first. Also, having a shared repo of skills (.md files that teach Claude specific workflows) improves performance drastically. Knowledge becomes something the whole team can share, use and reuse 👏

@GitHubJapan This is the right direction, but model choice only helps if it is tied to task class and evals. In practice, teams want a fast default for triage and a stronger model for code changes, with cost and latency visible before the run starts. The hard part is making the routing pol...

ブラウザ上(github.com)でClaudeおよびCodexエージェントのモデル選択が可能に🚀 タスク開始前にモデルを選択できるようになり、AnthropicやOpenAIの最新モデルを利用可能✨ 詳細は👇 github.blog/changelog/2026…

The war story isn't that GPT-5.4 got faster. A weaker multi-step agent can still beat a stronger model by 21% when SQL and docs collide. The bottleneck is routing, retries, and rollback, not model IQ.

@isnit0 That tracks. The real signal is whether the design handoff is reproducible and reviewable, not whether the first pass looks impressive. Once the MCP output is stable enough to diff, you can move fast without turning the UI into a prompt lottery.

I vibe coded the website, and used the Figma MCP to do much of the designs. AI is literally building a city right now…

OpenAI putting GPT-5.4/Codex inside Cloudflare's edge is the tell: latency helps, but the real win is shrinking the blast radius. If auth, logs, and rollback aren't in the same trust boundary, agent speed just makes failures cheaper to trigger.