In partnership with
Most of us run three or four coding agents and babysit them in three or four terminal tabs, switching windows every time one finishes a step. Orca just picked that lock. It puts Codex, Claude Code, OpenCode and Pi in one app, each in its own git worktree, so you can fan one prompt across several of them and keep the version that actually works. The fix isn't a smarter model. It's one less window.
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Sponsored
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The Popcorn Brand Turning Heads and Raising Capital
What does it look like when a consumer brand starts to break out?
Popsmith generated $5.8M in revenue in 2025 and is projecting $9M in 2026, with more than 100,000 customers and $20M+ in lifetime sales.
The brand has already earned national recognition through Oprah’s Favorite Things and partnerships with Costco, Sur La Table, and Williams Sonoma, where Popsmith is rolling out nationwide this holiday season.
Now Popsmith is raising capital to accelerate its next stage of growth, and individual investors have the opportunity to become shareholders.
The Drops
ToolOrca
99,554 stars · stablyai/orca
Runs Codex, Claude Code, OpenCode and Pi side by side, each in its own git worktree, tracked in one place.
Here is the real problem it solves. When you run more than one coding agent you lose time to window juggling, and comparing their output means reconstructing which one said what. Orca keeps each agent in an isolated worktree so you can fan one prompt across five of them and merge the winner without losing the others.
Quick start: see the README.
Catch: it's a desktop app plus a mobile companion, so you're adopting a new piece of software, not a script you drop into an existing terminal.
AffiliateHubSpot
If today's theme is agents running side by side so nothing falls through a window, the same logic applies to your customer record. HubSpot is a CRM with prebuilt AI agents that work on the data already in it: a Customer Agent handles customer questions around the clock, qualifying leads and resolving support tickets, with a Prospecting Agent and a Data Agent beside it. The free version never expires.
Use it for: putting an always-on support agent on the customer record your team already keeps.
We may earn a commission.
ToolClaude Code Studio
280 stars · arnaldo-delisio/claude-code-studio
Spins up specialized sub-agents so a single Claude Code conversation stops dying at the context limit.
Use it for: long builds where you keep re-explaining the project because the conversation hit its ceiling. Delegation keeps the main thread clean.
Catch: it's a config layer on top of Claude Code, not a replacement, so you're trusting a third-party agent structure with your project context.
RepoCCB
3,578 stars · SeemSeam/claude_codex_bridge
A visible, controllable terminal workspace for running Codex, Claude, Gemini and other CLI agents together.
Use it for: wiring agents into pipelines like A to B to C instead of running each one solo. The daemon keeps project state alive even after you close the window.
Catch: it's one more layer between you and the raw agent CLI, so a bug in the bridge is a bug in your workflow.
Repoweb-ocean
805 stars · Noniv/web-ocean
A real-time WebGPU ocean renderer built from scratch in the browser, no engine, no rendering library.
Use it for: proof of what one person and Claude Code can build now. The author says Claude Code wrote it, with separate critic subagents checking the renders. Open ocean.html straight from the file system, no server needed.
Catch: you need a recent Chrome with WebGPU and a capable GPU; on a laptop, force Chrome onto the dedicated GPU or it falls back to the integrated one.
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From Our Partners
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A fast AI answer isn't always a coordinated one. When AI resolves B2B issues without looping in the teams responsible for them, confidence outpaces alignment. A new Harvard Business Review Analytic Services briefing paper, sponsored by Front, looks at closing that gap.
Start Here
Turn a messy pile of customer questions into a real FAQ page in one pass
One prompt today instead of a how-to. It is one of ours, tested on ChatGPT and Claude. Copy it, paste it into the free app you already use, and fill in the three bits in capitals: the actual customer questions you've collected, what you sell and who buys it, and the handful of things you won't answer in public.
[CUSTOMER QUESTIONS]
What I sell and who buys it: [WHAT I SELL]
Things I am not willing to answer in public: [OFF LIMITS]
Turn this into a draft questions page for my website.
Rules:
- Group them into no more than six sections, and name each section in the customer's language rather than mine. "Shipping and delivery", not "Logistics".
- Merge questions that are the same question asked three ways. Write the merged version the way the most common one was asked.
- Answer each in under 60 words, speaking to the reader as you, and give the answer before any explanation.
- Every answer has to come from what is in my questions or what I told you above. Where you do not have enough to answer, write the question and put "NEEDS AN ANSWER FROM ME" underneath it instead of guessing at prices, times, or terms.
- Do not soften a hard answer into a vague one. If the honest answer is no, the page says no.
- No sales language and no invented deadlines anywhere on the page.
- Nothing from [OFF LIMITS] appears at all.
Give me, in this order:
1. The page, grouped, with the most-asked section first.
2. The questions nobody asked but should have, based on what I sell. Mark this list clearly as yours rather than theirs, five at most.
3. The three questions that came up most, and roughly how many times each.
4. Any message in my pile that points at something broken in the product or the process, rather than being a question at all.
The line that makes it work: "Where you do not have enough to answer, write the question and put NEEDS AN ANSWER FROM ME underneath it instead of guessing at prices, times, or terms." Most people let the model smooth over gaps with a plausible-sounding guess. This line forces it to flag the gap instead, so you fix the real hole in your FAQ instead of publishing a confident wrong answer.
TRY THIS: open your email or your support inbox right now, copy the last ten customer questions you actually got, and paste them in as the first blank.
This is one of 100 in the AIgent prompt pack. The other 99, plus guided builds with Mike on video, are inside the AIgent membership: Join the AIgent membership. Seven days free, then $29 a month. One click to stop.
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Recommended
Once your FAQ page is drafted, the next fix is getting those questions, your tasks, and your team's replies out of seven different tabs and into one place. ClickUp, one workspace for tasks, docs, and chat brings your tasks, docs, chat, and goals into one place so a small team stops switching between seven apps, free to start. We may earn a commission. |
Frontier Signals
Google just gave PyTorch an exit from Nvidia. TorchTPU is now open source, letting PyTorch apps move to Google's TPUs with a config change instead of a rewrite, the same move Amazon made a day earlier with TorchNeuron. If you've been locked into Nvidia pricing by default, you now have two credible ways out. (TechCrunch AI)
Meta's AI agent Muse exposed users' shared conversations through links anyone could find. Researchers enumerated 6,800 share links and, in the few hundred they opened, found confidential business notes, because each link carries the conversation's title where public DNS records can surface it. If you ship a share feature on anything that touches sensitive input, unguessable tokens aren't optional. (Ars Technica AI)
IBM shipped llm-d 1.0, a Kubernetes framework for running large models on your own hardware instead of a single vendor's cloud. IBM, Red Hat and Google lead it, with AMD, Cisco, Hugging Face, Intel and Mistral contributing, so this isn't a side project; it's becoming a shared layer for self-hosted inference. Worth a look if your model bill answers to one provider. (SiliconANGLE)
Spotify pulled over 50 million tracks it flagged as AI slop or spam from its recommendations in the past year. The platform now uses engagement patterns, upload behavior, and detection tools to flag slop before it reaches listeners. If your distribution channel is starting to filter AI output at this scale, assume every platform gets there eventually. (The Verge AI)
Meta is partnering with Apple to run its new open source model locally on Apple hardware. If the two biggest consumer platforms are pushing open models onto the device, plan for more of your AI to run without a cloud bill. (Stratechery)
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Which section do you open this issue for?
Before You Go
Orca's bet is that the bottleneck was never the model, it was the number of windows standing between you and comparing what five of them actually produced. Worth testing against whatever multi-agent setup you're already running.
What's the gotcha you'd hit running five agents against one prompt? Hit reply, I read every one.
See you Thursday.
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