So I don’t run this on every project. Depends on the size and how messy it is.

When the repo has real history (instruction files, SQLite, previous failed approaches, assets, handoffs, etc.), I stop trying to make one session carry the whole thing.

Claude Code stays in the terminal. It inspects the system, reads the instruction files, checks git and the last handoff, then writes a clean phased plan into a durable file.

Codex gets that file in the app and builds one phase at a time. File boundaries, verification steps, and the “do not touch” list are already there. I especially like it when I need browser control, live previews, or proof of the work.

The handoff is usually just:

GOAL
CURRENT STATE
SOURCE OF TRUTH
DECISIONS ALREADY MADE
PHASES AND FILE OWNERSHIP
VERIFICATION
DO NOT TOUCH

It's also super convenient because the remote control feature on the app works really well. (yes. You can do this with Claude Code also), it's great for keeping sessions running when you have to walk the dog. (in my case)

For smaller or cleaner work I just stay in one agent and feel it out.

Right now I’m mostly on Opus 4.6 (sometimes Fable 5) in Claude Code, and GPT-5.6 Sol at Extra High in Codex once the phase is locked.

I’ve seen other people doing versions of this Claude for context/planning, Codex for execution, file handoffs, cross-review.

so I’m posting because I want to know what the rest of you are actually running day-to-day.

Full split? One agent the whole way? Something else?

Lets see what’s working for people building real GTM systems.

A few weeks ago I had also built my own skill tree for my blog, so getting into MoltSets was fun because I could immediately compare how I was thinking about skills vs. how Adam was thinking about them.

I didn't just want to understand the library. I also wanted to understand the actual loop underneath it.

A product can easily give you data, but you still need people with skills to build those workflows that actually make sense.

Those skills help the next person get value faster, and eventually the users start creating the education and distribution around the product themselves.

I wanted to see if that idea actually held up on something messy inside Clearbox.

When someone signs up for Clearbox, we can join their posthog activity to their convex user and onboarding context.

Company domain? Easy.
Gmail or another free email? Professional identity trail basically disappears.

So I took 68 free-email signups and ran them through MoltSets.
54 returned an identity candidate.

13 resolved directly from the free-email address.

23 came back high or medium confidence after running them through
Clearbox’s own grader.

Everything else stayed in human review or unresolved. Which is actually what I wanted.

I don’t want enrichment that just confidently makes shit up because an agent found somebody with the same name.

The workflow has to earn the right to keep going.
Direct identity edge first. Only fall back to name search if there’s enough evidence.

I also built the data and cost model myself before letting an agent touch the workflow.

Because “free tokens” doesn’t really tell me anything.

Once I separated unlimited core data from the rolling request and record windows, then treated external tokens as their own pool for the expensive fallback work, I could actually understand what I was building around.

Four real usage meters instead of one vague credit number.

And the build gave me two actual skills:

Free-Mail Signup Resolution
Credit Guard & Token Economics

That’s when the MoltSets loop became real for me.
I went in to solve one Clearbox problem.

It produced a working resolver, a cost model, and something another builder could start from.

I’m packaging it into a public Notion guide with the flowchart, real redacted examples, all four usage meters, the guardrails, and both skills.

So keep following for the drop. Still iterating on it. Don't want y'all coming at me for any AI slop, so it's still on human review.

But feel free to DM me for early access! 😜

The mistake first...

I set the workspace up on Buzz (Block's open-source thing, humans and agents in the same channels). Built out the channels, wrote the canvas docs, seeded the first threads, posted the invite. People joined. Then it just sat there. Nobody posted anything except in #general.

I assumed the usual: people join stuff and never come back, that's the internet.

Went digging in the relay this weekend and pulled the membership roster per channel. Every channel I'd built had exactly one member. Me. Turns out when you accept a Buzz community invite, the desktop app auto-joins you to exactly two starter channels and nothing else. Every channel created after that has to be joined individually, and nobody knew they were there because they don't show up in your sidebar until you're in them.

So everyone who joined saw one empty room and reasonably concluded the place was dead. It wasn't dead, it was invisible. Fixed it by pulling the roster and adding everyone to every channel, which is a loop over buzz channels add-member. Wrote it as a script since it has to run for every new person who joins.

If you're running anything on Buzz, go check buzz channels members --channel <uuid> on a channel you made after launch. I'd bet money you have the same problem.

Now the direction change.

I keep getting asked how to become a GTM engineer. My answer is always the same three words: build in public. I still think that's right, but it's getting weaker, because text is now trivially fakeable. Anyone can generate a convincing thread about a pipeline they never built. I read ten of them a day. So can you.

Video is where that breaks down. You either screen-share the thing and explain why you picked the waterfall order you picked, or you can't. There's no prompt for having actually done it.

So I'm starting a show. GTM engineers walk through something they actually built, on screen, with the receipts. Not another interview about the future of AI in sales.

The what we should care about: I'm not just looking for people with audiences. I'm looking for people who built something real and have no idea how to show it. If that's you, that's the whole pitch. You bring the build, I handle recording and editing, and you leave with the episode plus a pack of captioned vertical clips to run on your own channels. No strings, no attribution games.

The workspace is the on-ramp. Post your builds there for a week or two, come on and walk through one. Not a gate, it's the prep. your posts end up being the episode outline.

Rebuilt the whole thing around that this weekend. Cut it from seven channels down to five, because seven channels for a handful of people is a ghost town by design. Rewrote the docs.

Invite's good for 30 days:

https://gtmelite.communities.buzz.xyz/invite/v2.Ob8-wOkY5qvDLRK2uuTra19aSzA5a8oj7hZNPVnXmY4

You need the Buzz desktop app for community features. Click the link, then go post what you're building.

And if you have a build you want on camera, just say so in the thread. First episodes are getting picked from whoever raises their hand.

Got invited on a podcast with my co-founder. The episode is still with their editors and won't hit YouTube for another two weeks. Old me would have waited for the link and then posted "excited to share" the day it dropped.

Instead I asked for the raw files and treated the transcript as the asset.

Whisper transcription with word-level timestamps. Then I had my coding agent read the transcript and find the story peaks. "Here's our take on automation" is a topic. "The AI told me API access takes 24 hours and forgot to mention the $10,000" is a story with a peak, and because the timestamps are word-level, the cuts land on words, not near them.

That got me 17 vertical clips with captions and hooks. Hosted them at public URLs, then used the Buffer API to stage drafts across TikTok, IG Reels, and YouTube Shorts. 33 drafts from one script. They stay drafts until I eyeball them, then it's one clip a day until the episode drops.

The daily LinkedIn layer runs off a Notion board my phone can operate. It tracks date, post text, video download, and status. Four minutes a morning with coffee.

Then the long-form pass over the same transcript produced a newsletter, a blog post, and this post. Every piece is a different altitude on the same hour of tape.

The podcast itself came from this exact motion. The host's team found my product through the content I was already posting. Nobody pitched anybody. The content did the outreach, then the recording became more content, which is presumably how the next one happens too.

One hour of recording became 17 clips, 11 daily posts, and 4 long-form pieces. About a month of cadence, and the episode itself is still in the queue as the finale.

Next time I would capture B-roll during the recording session. Screen captures of the actual tools we talked about would have doubled what the clips can show.

Anyone else mining recordings this way? Curious what your transcript-to-asset ratio looks like, because I think ours is still conservative.

ps. For all my builders out there who are trying to figure out a way to build in public, take these record yourself working, take podcasts, webinars, or whatever you can do to showcase your natural talents. That will set you apart from the rest and make sure you never have to drop a CV.

That's one of the main goals I want from any of you, any of y'all in this community: to really learn how to build in public to the point where you're so in demand that you never have to drop another CV.

If you're trying to figure out how to do these recordings and edits programmatically, you know where the scripts are. If you're new here, just DM me and I'll send them to you.

That's me signing out, Shawn Tenam, go-to-market engineer and co-founder of clearbox.

P.S. Special shout-out to the LeanScale team. Beyond putting together an incredible podcast and media experience, their founders genuinely care, and their team has some seriously sharp RevOps and go-to-market engineers. They understand GTM at a level most agencies simply do not. If you need a partner for RevOps or allbound growth who will actually care about the outcome, I could not recommend them enough.

Being a go-to-market engineer today isn’t just about knowing Clay, Claude, or your favorite workflow.

It’s about staying ahead of the curve and figuring out which new tools are actually worth your time before they become mainstream.

Every week there’s another “game-changing” AI product.

Instead of arguing about them on Reddit or waiting for YouTube reviews, why don’t we test them ourselves?

I’ve been experimenting with Buzz, Jack Dorsey’s new open-source workspace for humans and AI agents.

It feels like a mix of GitHub and Slack, but built around collaboration instead of just chat.

I’m thinking we create a shared cloud workspace where nobody owns it. We use it to evaluate new tools, share workflows, build together, and answer one simple question:

Is this worth adding to the GTM engineering toolkit, or does it belong on the Fraud Alert list?

The only prerequisite I’d ask is:

• You have a GitHub repo (or you’re actively building one).

• You’re interested in GTM engineering, AI workflows, automation, or building systems.

I genuinely don’t care if your repo has 0 stars or 10,000.

This isn’t about clout. It’s about builders learning from builders.

If that sounds like something you’d want to help shape from day one, let me know.

I’d love to get a small group together and see what we can build.

Been posting my coding-agent GTM workflows here for a while. The question every time is which data vendor I use. After four months of wiring different ones into pipelines: I start every run at Apollo, and the reason is the API plus a coding agent writing to a database I own.

This replaced a stack of tools I used to pay for.

I take a list of target companies - a screenshot from a market map, a conference exhibitor page, my own LinkedIn following - and hand it to Claude Code. It resolves domains, runs organizations/enrich?domain= for the org ID and firmographics, then mixed_people/api_search filtered by organization_ids for the people. That search is free. The redacted preview gives me title and person ID with the name obfuscated, so I qualify the whole set before spending a credit, then pull only the rows I want.

Every enriched row writes to a local SQLite database. SQLite is my own contact store, the CRM before the CRM. It holds every company, every contact, every enriched field, on my own machine. Apollo's API is what lets me build it, and I can push it to the cloud or a real CRM whenever I want. The expensive part of sourcing was never the data, it was the manual work of getting the right rows into something I own.

Numbers and mechanics.

• The search is free. Credits only go on the rows you pull.
• On flagged inventory, bulk_match returns verified work emails at around 99% for me, instant.
• Long runs go to a Codex session on a remote instead of my laptop. Start a few-thousand-row job, come back to a filled database.

On vendors, honestly. No source is perfect. But Apollo is the first layer. It is the whole data layer - the roster, the org record, the current-company signal, and the reveal in one place. Apollo is where you start. A lot of the tools people pay more for are reselling Apollo's data underneath anyway.

Two gotchas from building this.

organization_ids is the only reliable filter in mixed_people/api_search. A domain or company-name filter returns people unconnected to your targets. Enrich the company first, always.

bulk_match draws from the data/export credit pool and caps at ten per batch. Check your balance before a multi-segment run or it stalls mid-job.

Happy to share the Python for the screenshot-to-SQLite loop.

Building in the open at.....you know where

My product is closing in on 100 signups with $0 on ads, and the biggest driver is outreach to my own network. Not cold lists. Sharing the full build because I'd want to read this here.

Stack: PhantomBuster, Apollo, Claude Code, SQLite, DeepLine, HeyReach.

Step 1: Export your network. PhantomBuster pulls your LinkedIn connections and followers into a list. Nothing new, everyone does this.

Step 2: Export your DMs too. This is the step people skip. Your message history IS the relationship data. A connection list tells you who's there. The DMs tell you what actually happened between you.

Step 2.5: Apollo enriches everything entering the database. Every record, network export and signups alike, gets company, title, and contact data before anything else touches it. This is critical. The subagents can't categorize on a name and a profile URL, and you can't write a real message without knowing where someone works now.

Step 3. Claude Code with subagents. I pointed Claude Code at both exports (everything lives in SQLite) and had subagents categorize every single person:

- people who've pitched ME

- people I'VE pitched

- mutuals I've actually talked with

- engagers who never DM'd

- dormant connections

Each subagent takes a slice, categorizes, writes back to the database. What would've been weeks of manual review runs while you make coffee.

Step 4: The category writes the message. Someone who pitched you? They already wanted the conversation, easiest reply in the world. Someone YOU pitched before? Do not re-pitch, come in with something useful instead. Engagers who never DM'd are warmer than you think. One list, five different messages.

Step 5: HeyReach sends 40 connection requests a day off the prioritized list. Sends are the only automated part. Every conversation after that is me, with the actual message history as context.

Step 6: The same loop runs on signups. Every signup lands in the same SQLite database, Apollo enriches them, DeepLine finds their LinkedIn, and I DM each one personally, written around what they're actually running into on Reddit. No automation, no template. Signup to founder DM, same day.

Results so far: 25%+ reply rate, zero "not interested" replies.

Point is: your own network is the warmest list you'll ever have, and most people blast it with one generic message or ignore it entirely. The categorization is the whole game.

Happy to go deeper on any step, including the subagent setup.

Disclosure: I'm Shawn and the product is Clearbox It finds the Reddit conversations that matter for your ICP by reading intent, not matching keywords. GummySearch's founder tested it and recommends it as they sunset. That's it, no link unless someone asks.

For those of you already following the daily. tonight’s episode is up and it’s a good one.

Biggest news: GPT 5.6 Sol dropped and Anthropic responded by resetting every usage limit (hourly and weekly) across all tiers.

The Claude subs are in full celebration mode. Also covered the wild $80 crypto challenge to Fable 5, a solid Three.js Codex plugin, and the HANDOFF.md context trick that’s actually useful.

Claude Code Daily — Friday July 10

On the tooling side, a few people have asked about the scraper behind it.

This one is intentionally lightweight. Claude Code rebuilt the transport layer (Playwright + BeautifulSoup on old.reddit) after the last round of blocks. For a daily blog digest it’s perfect if it dies tomorrow, I just fix it again. No customers impacted.

Clearbox is built very differently. When users are paying for reliable high-intent signals every single day, you can’t afford flaky scrapers or surprise blocks. That’s where Aura’s full pipeline comes in: better resilience, cost controls, residential handling, monitoring, recovery, the whole thing.

Huge difference between fun daily content scraper and production GTM inbox your users depend on.

I’ll keep sharing more of the practical lessons as we open up the playbook. safe scraping patterns, cost numbers, what actually holds up, etc. Appreciate everyone building in public with me.

What data sources are you all scraping/using for your own GTM motions right now?

Hey guys. I've been sharing my builds and creator experiments in here for a while, and I finally put it all on YouTube. Just dropped episode 1.

It's my actual GTM dev workflow.

Claude Code in the terminal, Codex in the app, running both so I never hit an API limit, plus context engineering, orchestrating sub-agents through Apollo, /effort max, and using SQLite as memory. I use all of it to build Clearbox (a Reddit opportunity inbox that reads intent, not keywords) in public.

straight up the audio and video editing is rough. I'm still figuring that side out, so please trash me on it. If you have tips on editing, pacing, thumbnails, whatever, I'm all ears.

I'll be honest, part of this is me promoting myself. But I genuinely love sharing this stuff and giving feedback back just as much. If any of it is useful to your own builds, that's the win. Ask me anything about the setup and I'll go deep.

Anyone can use AI to write content.
I think the better play is using AI to capture and distribute what you’re already doing.

Current stack:

OBS to record workflows while I’m building or testing stuff.

Open Shorts to clip longer recordings into usable short-form clips.

Deepgram as a backup transcript layer.

YT Dip for pulling YouTube clips when I’m doing marketing research or looking at how people package certain ideas.

Claude Code for building little tools, testing workflows, connecting APIs, and moving faster.
DaVinci Resolve for editing. Haven’t worked Higgs Field into the flow yet, but that’s next.

Buffer API + Supabase for distribution. This one is underrated. Buffer lets you connect up to 3 channels for free, so I’m starting to build a simple content posting system around it instead of manually posting everywhere.

Clearbox / Reddit for research. Reddit is especially useful because people say things in the exact language they actually use. I use it for inspiration, but also to sanity-check claims before I say something publicly.

RapidAPI for niche-specific research. Example: I have a client selling graphic design to churches, and that market has specific language. You can’t just throw generic marketing copy at it and hope it lands.

Super Whisper + Whisper Flow are probably the biggest unlock. I can talk through an idea, workflow, or client problem, then turn that into something structured without losing my actual voice.

Fireflies.ai recording my calls, python scripts in the background pulling them into discord in channels for different content distribution is probably what makes this whole thing

Shawn Tenam GTM ENGINEER & CO FOUNDER @ clearbox "your reddit opportunity inbox"

Yo, what's up, builders!! Dropping Founders Journey, my official YouTube series..

if you've been following so far, you might already know what this all is about. basically building in public, taking it to the next level where i actually share more than just the scripts from the claude code session i run. now you guys get to see them live.

this is also one of the Best Claude Code tip I have: hook it to a SQLite DB at your project root.

Store your context in columns. Now the agent reads columns instead of the whole folder.
That's how you stop blowing through your context window.

Kicking off a new founder’s journey series: building Clearbox’s email infrastructure from the ground up.

33 domains. Isolated warming account. Full in-house orchestration. Dashboard visibility.

Showing the actual prompts and decisions in real time.

Follow for every step.

Sorry for the horrible audio, guys!! follow the series to not only see me share my builds but also the production up-skilling that goes into it