Lenny RachitskyModify

2026-07-13 23:02:17

What a harness is and how to build one with Claude Agent SDK

Brought to you by:

Bolt.new—Turn your idea into a real product

Customer.io—Build customer engagement campaigns from a single prompt

Claire explains why harnesses matter and when they’re better than general-purpose tools like Claude Code or Codex, and walks through the custom Claude Agent SDK harness she built to automate Sentry bug triage at ChatPRD. You’ll see how she structured the workflow, encoded permissions, connected tools like Sentry and Linear, and turned a repeatable engineering task into something an agent can run more consistently every time.

Biggest takeaways:

1. A harness is just code around an AI agent—nothing more mysterious than that. The term has taken on an almost mythical quality in engineering circles, but I strip it down in this episode: a harness is code you write to make an AI agent more effective at a specific job. Cursor is a complex harness. Claude Code is a complex harness. Yours can be eight files and a terminal UI.

2. Build a harness when the same workflow needs the same setup and the same outcomes every time. The trigger is recognizing a job that is partly deterministic (defined steps, defined tools) and partly non-deterministic (the AI figures out root causes and writes the report). Sentry bug triage qualified because every investigation follows the same evidence-gathering process and ends with the same artifact bundle.

3. Opinionated tool adapters beat general MCP access for specialized workflows. Rather than giving the agent broad access to the Sentry MCP and letting it wander through traces, I built a custom Sentry adapter that pulls exactly what matters for a bug report and nothing else. That specificity makes the agent faster, cheaper, and less likely to go off-script.

4. Encoding permissions in the harness removes the need to prompt them every single time. In a general-purpose coding tool, you have to remember to say “investigate only, do not write code.” In my harness, that is a flag in the interface. I click “investigate,” paste the Sentry link, and the agent already knows its constraints without being told.

5. Structured artifacts are what separate a one-off investigation from a team-wide resource. Every time my harness runs, it outputs a task log, a Sentry issue brief, relevant logs, a worker report, and an HTML summary file. That artifact bundle means the engineering team gets a consistent, scannable record of every bug investigation without anyone having to write it up manually.

6. A harness lets you do multi-model routing in ways a single general-purpose tool never could. Claude Code is Claude. Codex is GPT. A custom harness using the Claude Agent SDK lets you pick the right model per step, enforce different tool policies per invocation, and swap models over time without changing how the interface works. That flexibility is one of the strongest arguments for owning the harness layer yourself.

7. The open chat field has been good enough, until it stopped being good enough. I acknowledge in this episode that just typing into Claude Code has produced real work. But this marks a shift in my thinking: general-purpose agents are now better used to orchestrate specialized harnesses than to do every job themselves. Giving a constrained agent a specific harness gets more consistent output than giving a powerful agent an open prompt.

This solo builder runs 24/7 local AI on his own hardware | Alex Finn

Brought to you by:

Runway—The creative AI platform for images, video, and more

Jira Product Discovery—Prioritize with insights, build with confidence

Claire talks with Alex Finn about how he built a 24/7 local AI fleet using Mac Studios, a DGX Spark, an RTX 5090, and a custom dashboard to keep agents running around the clock. Alex breaks down what each machine is actually good for, how he routes work across local models like GLM, Qwen, and Ornith, and why “unlimited inference” changes the entire economics of AI workflows. They also get into his Claude Code build-and-review loop, his OpenClaw and Hermes setup, and the surprisingly practical playbook behind running your own always-on software factory.

Biggest takeaways:

1. The case for local AI isn’t ROI; it’s unlimited inference. The math on a $10,000 Mac Studio vs. a $20 ChatGPT subscription only looks crazy until you run an agent 24/7. At that scale, cloud APIs get expensive fast, and local models running around the clock open use cases that simply aren’t economically viable otherwise. Alex runs security scans, code reviews, and social signal monitoring on a continuous loop that would cost thousands a month in cloud credits.

2. Each hardware tier has a job. Mac Studio handles massive models slowly but at Opus-level intelligence (Alex runs GLM 5.2, which he calls Opus 4.8-equivalent, on a single Mac Studio). DGX Spark is the sweet spot: 128 GB of Nvidia unified memory plus CUDA speed, enough for models like Qwen 3.6 running fast. The RTX 5090 has only 32 GB of VRAM but is cloud-speed fast. Buy for the task, not the spec sheet.

3. Tailscale is the connective tissue for a multi-machine setup. Once all your machines are on the same Tailscale network, one agent (OpenClaw or Hermes) can hop across them, check the hardware, load the right model, and get it running without any manual configuration. Alex says there’s truly no technical knowledge required once Tailscale is installed, and he recommends it even if you only have one machine, because it also lets you test local apps from your phone.

4. Local models are the BDR; Claude Code is the closer. Alex’s security scanning loop is a good example of the hybrid model that actually works. A local model (GLM 5.2) scans code every 20 minutes and dumps findings into a Markdown file. Claude Code checks that report once a day and decides what’s real and worth fixing. The local model does the volume work cheaply; the frontier model does the judgment work precisely. Trying to run Claude Code every 20 minutes instead would cost thousands a month.

5. The software factory runs on two loops and a rocket emoji. Every morning, Alex does a planning session in Claude with a “morning build” prompt that produces a task list for his SaaS. The build loop picks those up and starts shipping. The review loop checks the work. When something passes review, Alex gets a Slack ping, and leaving a rocket emoji on it triggers an automated merge. He goes from morning brief to reviewing and merging code without touching the keyboard again until he does the approval round.

6. OpenClaw and Hermes fill different needs, and you probably want both. Alex prefers OpenClaw for the “big bang” wow moments and the emotional connection (his words). But Hermes has been more reliable under repeated updates. His solution is redundancy: three Hermes agents and two OpenClaw agents running simultaneously, so when three of the five are broken (which happens), the other two can fix them. The failover is deliberate, not accidental.

7. Task allocation by model intelligence is the skill that makes the fleet useful. GLM 5.2 is Opus-level smart but painfully slow, so it gets the deep, latency-tolerant work. Qwen 3.6 is quick and good enough to read Twitter for product signals. Ornith 1.0, a Qwen fine-tune with reinforcement learning baked in for coding, has beaten Qwen on every eval Alex has run and runs comfortably on a DGX Spark. The insight is that “smartest model everywhere” is wasteful; matching model intelligence to task complexity is what makes ambient AI economically coherent.

8. The vague posting about loops is partly a competitive moat. Alex’s theory: the companies building the best AI coding infrastructure (including OpenAI and Anthropic themselves) have internal loop systems that are their last real competitive advantage. If you can pump out high-quality code faster than anyone else because your build-review loop is better, you don’t go publishing a how-to. Claire’s counter-theory: most people vague-post because their loops are boring and vagueness gets more engagement than specifics. Both are probably true, depending on who’s doing the posting.

GPT-5.6 Sol vs. Claude Fable: Why OpenAI’s new model crushes my benchmark

Claire puts GPT-5.6 Sol head-to-head with Claude Fable, Sonnet 5, and the rest of the GPT-5.6 lineup using her own five-part benchmark for real product work. The result: Sol becomes her new daily driver. Claire breaks down exactly why and also shares where she’d still use Terra, Sonnet, or Fable instead.

Biggest takeaways:

1. GPT-5.6 Sol is the most practically effective model I’ve tested, even if Fable is theoretically smarter. I ran a five-category benchmark across PRDs, prototypes, wireframes, debugging, and agentic voice, and Sol had the highest taste score by a significant margin on the 70% Claire/30% machine split. That gap between “hyper-intelligent” and “actually ships” is real, and for product work Sol wins.

2. Full-fidelity prototypes from Sol are more functional and more opinionated than anything else I’ve tested. Across a doc scheduler, a dev tools incident triage site, and a consumer habit tracker app, Sol consistently produced designs with better visual hierarchy, semantic color use, and working interactivity. Fable’s outputs were fine; Sol’s were the ones I’d actually show a stakeholder.

3. Sol’s writing is just easier to work with. Fable writes like it has never met a human before, incredibly pedantic and almost inscrutable when you need to collaborate. Sol writes like a normal person, and that difference compounds fast when you’re iterating on PRDs or talking to an agent all day.

4. For PRD writing specifically, GPT-5.6 Terra might be the better pick. I asked Sol to greenfield-rebuild my approach to PRDs for 2026, and while Sol’s output was excellent, Terra’s clean, direct, no-frills business writing made me think it’s the right call when you want crisp, fast documentation without extra flair.

5. Fable gets too locked in its own frameworks; Sol is willing to reconsider. I had a hardened tool-calling loop in my prototyping product that only GPT-5.5 could run. Fable insisted it was a model problem and refused to budge. The moment I switched to Codex and told it to just fix it, Sol got Sonnet 5 working in one shot. That kind of practical flexibility is exactly what you need when building real products.

6. Sonnet 5 is still my favorite for agentic voice in Open Claw. Even after this whole benchmark, I gave Sonnet 5 a gold star for voice: aside from the dashes, it sounds the most human. I use Sonnet for my OpenClaw and I’m not changing that. Sol did a worse job on agentic voice overall, and I still can’t get GPT models to run well in my OpenClaw setup.

7. GPT-5.6’s video editing via Codex is one of my favorite new workflows. I dropped in a full recording from a talk I gave at Cursor’s event, asked for five hype-video clips, gave feedback on pacing and orientation, and had shareable social clips I could drop into CapCut in a fraction of the time. This use case alone justifies experimenting with GPT-5.6.

8. Browser use with Codex plus GPT-5.6 and @Chrome is the best agentic workflow I’ve found. I opened LinkedIn, told it to reply to high-value messages from executives and ChatPRD fans, and it burned through roughly 500 messages. I’ve also used it to test web apps and fill out forms. When I got rolled back to GPT-5.5 temporarily, my life was measurably worse. Learn @Chrome and just let it rip.

9. The “forest green” tell is real, so name it in your prompts. Sol has a strong aesthetic bias toward what feels like a woodland-themed palette hardcoded somewhere in its system. You will see a lot of green. I told the OpenAI team, I’m noting it here, and I’m already prompting against it when I want a different aesthetic direction.

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

2026-07-13 20:05:20

Alex Finn is an AI builder, YouTuber, and the creator of Vibe Code Academy, a community for people learning to build with AI tools. He runs one of the most ambitious local AI setups I’ve come across: three Mac Studio 512 GB machines, a DGX Spark, and a custom RTX 5090 build, all coordinated through a fleet dashboard he built himself. He’s spent five months figuring out which local models belong on which machines, how to wire them to Claude Code loops, and how to get a software factory running without babysitting it.

What you’ll learn:

1. How Alex chose between a Mac Studio (512 GB unified memory), DGX Spark, and RTX 5090, and what each is actually good for

2. Why Tailscale is worth installing even on a single machine, and how it lets one agent manage your entire hardware fleet

3. How the build loop and review loop in Claude Code work

4. How to allocate tasks by machine and model

5. Why unlimited local inference changes the use-case math in a way a $20 cloud subscription never can

6. What OpenClaw and Hermes are each best suited for, and why Alex runs five agents total with failover baked in

Brought to you by:

Runway—The creative AI platform for images, video, and more

Jira Product Discovery—Prioritize with insights, build with confidence

In this episode, we cover:

(00:00) Intro

(02:58) Alex’s hardware stack

(03:48) What “ambient AI” means

(04:15) Alex’s red-pill moment with OpenClaw

(07:04) Mac Studio vs. DGX Spark vs. RTX 5090

(13:24) How to set up local models with no technical knowledge (Tailscale + OpenClaw/Hermes)

(17:16) Fleet control dashboard: assigning 24/7 tasks across machines

(20:42) Local models as security scanners feeding Claude Code

(22:25) How Alex allocates GLM 5.2, Qwen 3.6, and Ornith 1.0 by task

(24:28) OpenClaw vs. Hermes: the honest comparison

(26:55) The software factory: build loop, review loop, rocket emoji

(31:55) Lightning round: favorite hardware, favorite model, prompting style

(34:46) Where to find Alex

Tools referenced:

• Claude Code: https://claude.ai/code

• OpenClaw: https://openclaw.ai/

• Hermes: https://hermes-agent.nousresearch.com/

• Tailscale: https://tailscale.com/

• Codex (OpenAI): https://openai.com/codex

• GLM 5.2 (z.ai): https://huggingface.co/zai-org/GLM-5.2

• Qwen 3.6 (Alibaba): https://huggingface.co/Qwen/Qwen3.6-35B-A3B

• Ornith 1.0: https://github.com/deepreinforce-ai/Ornith-1

• Gemma 4: https://huggingface.co/collections/google/gemma-4

• Playwright (browser testing): https://playwright.dev/

• Vercel (preview deploys): https://vercel.com/

Other references:

• DGX Spark (Nvidia): https://www.nvidia.com/en-us/products/workstations/dgx-spark/

• Mac Studio (Apple): https://www.apple.com/mac-studio/

• How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex: https://www.lennysnewsletter.com/p/how-to-design-ai-agent-loops-schedules

Where to find Alex Finn:

LinkedIn: https://www.linkedin.com/in/alex-finn-1848684a

YouTube: https://www.youtube.com/@AlexFinnOfficial

X: https://x.com/AlexFinn

Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

LinkedIn: https://www.linkedin.com/in/clairevo/

X: https://x.com/clairevo

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2026-07-12 20:32:14

Noam Segal is a longtime research leader across Airbnb, Meta, Twitter, Zapier, Intercom, and Figma, a certified coach, AI builder, and my community research lead. Together, we run the annual Tech Worker Sentiment Survey, now in its second year and one of the largest of its kind: a quantitative study of how people in tech actually feel about their jobs, AI, burnout, and the future of their careers. This year’s survey captured responses from thousands of workers across product, engineering, design, research, marketing, data, and sales, and the results are striking.

In our in-depth conversation, we discuss:

1. Why AI has split the tech workforce almost exactly in half—one half that’s thriving, another that’s shaken

2. The four emotional archetypes defining tech workers right now (the Energized, the Conflicted, the Disoriented, and the Resentful)

3. Why burnout has jumped an alarming 11 points in a single year

4. Why nobody in tech would recommend their job to someone entering the industry today

5. The #1 fear in tech right now (it’s not job loss to AI)

6. Why managers are the single biggest lever for employee well-being

7. Concrete advice for what employees and leaders can do right now

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

Mercury—Radically different banking, now with Command

Where to find Noam Segal:

• X: https://x.com/noamseg

• LinkedIn: https://www.linkedin.com/in/noamsegal

Referenced:

• How tech workers are feeling in 2026: a workforce splitting in two: https://www.lennysnewsletter.com/p/how-tech-workers-are-feeling-in-2026

• How tech’s most resilient workers handle burnout: https://www.lennysnewsletter.com/p/how-techs-most-resilient-workers

• Please stop the AI Confidence Theater: https://www.elenaverna.com/p/please-stop-the-ai-confidence-theater

• Velocity over everything: How Ramp became the fastest-growing SaaS startup of all time | Geoff Charles (VP of Product): https://www.lennysnewsletter.com/p/velocity-over-everything-how-ramp

• NPS Is The Worst: https://www.npsistheworst.com

• The Terminator: https://www.imdb.com/title/tt0088247

• Skynet: https://terminator.fandom.com/wiki/Skynet

• Inside Devin: The world’s first autonomous AI engineer that’s set to write 50% of its company’s code by end of year | Scott Wu (CEO and co-founder of Cognition): https://www.lennysnewsletter.com/p/inside-devin-scott-wu

• Devin: https://devin.ai

• An AI state of the union: We’ve passed the inflection point, dark factories are coming, and automation timelines | Simon Willison: https://www.lennysnewsletter.com/p/an-ai-state-of-the-union

• Redeploying Fable 5: https://www.anthropic.com/news/redeploying-fable-5

• Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google): https://www.lennysnewsletter.com/p/why-half-of-product-managers-are-in-trouble

• Inside Linear: Building with taste, craft, and focus | Karri Saarinen (co-founder, designer, CEO): https://www.lennysnewsletter.com/p/inside-linear-building-with-taste

• Building beautiful products with Stripe’s Head of Design | Katie Dill (Stripe, Airbnb, Lyft): https://www.lennysnewsletter.com/p/building-beautiful-products-with

• The design process is dead. Here’s what’s replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead

• OpenAI Codex lead on the new shape of product work | Andrew Ambrosino: https://www.lennysnewsletter.com/p/openai-codex-lead-on-the-new-shape

• Elon Musk: ‘Chances are we’re all living in a simulation’: https://www.theguardian.com/technology/2016/jun/02/elon-musk-tesla-space-x-paypal-hyperloop-simulation

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

Read more

2026-07-12 00:40:58

👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

Read more

2026-07-10 01:33:57

GPT-5.6 Sol is back, and I ran it through my full How I AI vibe benchmark against GPT-5.6 Terra, Luna, Claude Fable 5, and Sonnet 5 across five categories: PRDs, prototypes, wireframes, debugging, and agentic voice. Sol won by a meaningful margin on my Claire Weighted Index (70% my taste, 30% Terminal Bench 2.1), and I also tested two use cases I can't stop thinking about: building a gamified homework tracking app for my kids in one shot with Codex, and browser automation with Chrome that burned through 500 LinkedIn replies while I did literally nothing.

What you’ll learn:

1. How I scored five AI models (including GPT 5.6 Sol, Fable 5, and Sonnet 5) using my “Claire Weighted Index” benchmark across PRDs, prototypes, code, and agentic voice

2. The difference between GPT-5.6 Sol (Terra) and Sol for PRD writing

3. How Fable’s precision and pedantry made it harder to collaborate with, and the exact moment Sol broke through where Fable got stuck

4. Why Sonnet 5 is still my go-to for agentic voice in OpenClaw, even after this whole benchmark

5. How I used GPT-5.6 Sol in Codex to build a fully gamified homework tracking app for my kids in one shot

6. The video editing use case that saved me hours clipping a talk I gave at Cursor’s event

7. How to use Codex plus GPT-5.6 and Chrome for browser automation, and why this is my single most-loved use case right now

In this episode, I cover:

(00:00) Intro

(01:10) The three GPT-5.6 models: Sol, Terra, Luna

(02:17) Pricing: Sol vs. Fable API costs

(03:24) The How I AI benchmark

(05:03) Claire-weighted Index results

(07:00) Per-task winners: prototypes, PRDs, agentic voice

(11:59) What Claire actually rewards

(13:20) Full-fidelity prototype side-by-sides (Sol vs. Fable)

(17:45) Wireframes

(18:19) Agentic voice

(19:15) Where Sol is better than other models

(23:56) Gamified kids’ homework app, built in one shot

(28:02) Fable’s pedantry problem and how Sol broke through it

(31:49) Two bonus use cases: video editing and browser use

(35:08) Final summary and model recommendations

Tools referenced:

• GPT 5.6 (Sol, Terra, Luna): https://help.openai.com/en/articles/20001325-a-preview-of-gpt-56-sol-terra-and-luna

• Codex: https://openai.com/codex

• ChatPRD: https://www.chatprd.ai/

• CapCut: https://www.capcut.com/

• Math Academy: https://www.mathacademy.com/

Other references:

• Cursor event where Claire spoke on the future of PM: https://www.youtube.com/watch?v=4CAFK-rc26A

• ChatPRD blog (where benchmark outputs will be published): https://www.chatprd.ai/

Where to find Claire Vo:

ChatPRD: https://www.chatprd.ai/

Website: https://clairevo.com/

LinkedIn: https://www.linkedin.com/in/clairevo/

X: https://x.com/clairevo

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

2026-07-09 20:32:21

Adam Mosseri is the Head of Instagram, where he oversees an app used by over 3 billion people. He also leads the team building Threads. Adam has run Instagram for longer than its founders did, after taking over from Kevin Systrom and Mike Krieger in 2018. A designer by training, he spent over 15 years at Meta, starting as a designer on Facebook’s mobile app, rising to lead Facebook’s News Feed, and eventually chosen to lead Instagram. During his tenure, Instagram’s user base has more than tripled.

In our in-depth conversation, we discuss:

1. How the canonical product team structure is changing in 2026, from baker’s-dozen specialist teams to lean pods of four to six generalists

2. The rise of the “product staff” role—a blending of PM, design, data science, and research into one generalist operator

3. Why Adam is bullish on designers even as functional boundaries dissolve, and which roles are most at risk

4. What the Instagram algorithm knows about you, and why it’s only now catching up to what people assumed it knew years ago

5. Why the rise of AI-generated content is a tailwind for Instagram, and how the company is thinking about creator identity in a synthetic-content world

6. The two biggest product failures of Adam’s career—Facebook Home and the first version of Reels

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

Mercury—Radically different banking, now with Command

Where to find Adam Mosseri:

• X: https://x.com/mosseri

• LinkedIn: linkedin.com/in/mosseri

• Instagram: https://www.instagram.com/mosseri

• Threads: https://www.threads.com/@mosseri

Referenced:

• What happens after coding is solved? | Fiona Fung (Manager of the Claude Code and Cowork Teams): https://www.lennysnewsletter.com/p/building-the-most-ai-pilled-engineering

• Claude Code: https://www.anthropic.com/product/claude-code

• Claude Cowork: https://www.anthropic.com/product/claude-cowork

• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens

• A rational conversation on where AI is actually going | Benedict Evans: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where

• OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai

• Mythos: https://www.anthropic.com/claude/mythos

• Fable: https://www.anthropic.com/claude/fable

• Pluralistic: The Reverse-Centaur’s Guide to Criticizing AI: https://pluralistic.net/2025/12/05/pop-that-bubble

• Plastic Dream Sequence on Instagram: https://www.instagram.com/plasticdreamsequence

• TikTok: https://www.tiktok.com

• Facebook–Cambridge Analytica data scandal: https://en.wikipedia.org/wiki/Facebook%E2%80%93Cambridge_Analytica_data_scandal

• Facebook Home: https://en.wikipedia.org/wiki/Facebook_Home

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

Read more