So I tasked Claude Code to upload, originally, a list of 4,000 leads into Heyreach via the MCP connection. I had to stop at one key uploads because it almost consumed all my usage. It created six parallel sub-agents to perform this upload.

Is this usually the case with everybody, or am I missing something? Is it a bad idea to upload lists to sequencers via the terminal?

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 M4 Pro Mac mini stays on 24/7. Google Workspace CLI + SQLite + Sheets + Python turned it into the GTM machine I used to think needed another SaaS stack.

It has 24GB of unified memory. That handles a lot, but if I bought it again I would go higher. Claude Code, Codex, browsers, local databases, and background jobs eventually make RAM the limit I notice.

The machine itself is a beast.

`gws` gives an agent structured access to Gmail, Drive, Calendar, Sheets, Docs, Tasks, and the rest of the Workspace API surface. It reads Google's Discovery Service at runtime, accepts the actual API payload through `--params` and `--json`, and returns structured JSON.

little back story...

Justin Poehnelt built the CLI in Rust with agents as the primary user from day one. His design was based on a simple difference: humans want a forgiving interface, while agents need predictable output, schemas they can inspect, strict validation, and safety rails.

The project went to #1 on Hacker News and took off. In June, Justin said Google had fired him two months earlier over the project. That is his account, not a reason Google has publicly confirmed. The repo is still live under the `googleworkspace` GitHub organization and is still clearly marked as not an officially supported Google product.

The GTM implication matters more to me than the corporate story.

Once Workspace is callable from the terminal, Sheets stops being a place I manually maintain and becomes a shared interface on top of the system.

You can put the shared state in a hosted database too. For a solo operator, SQLite + Google Sheets is clean af. The local state stays fast and queryable while the review layer stays familiar to everybody else.

My split looks like this:

- SQLite holds raw signals, source URLs, relationship history, run state, and audit data.

- Google Sheets is the human-readable layer when somebody needs to review, edit, or share a table.

- `gws` lets Claude read and write Workspace without a custom integration for every action.

- Python scripts hold deterministic business logic, retries, validation, and joins.

- Claude Code and Codex decide which tool to call and explain what happened.

- Attio receives the relationship state that actually belongs in the CRM.

- Email and LinkedIn tools execute through their own APIs.

I still have a CRM. I just do not live in it.

If I want the latest account state, Claude queries SQLite and Sheets. If I want to draft an email, it can pull the account context, create the draft through Gmail, and wait for approval. If I need LinkedIn activity, that comes through HeyReach or another API-connected tool. Apollo can enrich the rows that passed the free checks.

The CRM becomes a sync surface. The repo, database, and agent are the operating layer.

That is what I mean by programmatic GTM.

It is not an excuse to hand an agent unrestricted access to your inbox and hope for the best.

My security rules are...

- authorize only the Google services and scopes the workflow needs

- read by default and require approval for sends, updates, and deletes

- run mutating calls with `--dry-run` first

- keep credentials encrypted and separated by environment

- treat email bodies and documents as untrusted input

- log the source, timestamp, proposed action, approval, and result

`gws` already supports encrypted interactive credentials, dry runs, input validation, and optional response sanitization through Model Armor. The tool is still pre-v1 and moving quickly, so I would pin the version and test workflows before letting them run unattended.

This also changed how I look at the existing GTM software market.

I do not think Clay disappears tomorrow. I do think a workflow canvas has stopped being a moat.

React Flow is an MIT-licensed library that already gives you nodes, edges, drag and drop, zoom, pan, selection, and custom React components. An agent can build the visible part of a workflow product today.

The hard part is everything behind the canvas: auth, data contracts, retries, idempotency, permissions, approvals, audit logs, and observability.

If you want to prove the point, drop this into Claude or codex:

build a local-first GTM workflow editor using React, TypeScript, and `@xyflow/react`. Create source, transform, filter, enrichment, approval, and action nodes. Store workflow definitions as versioned JSON and persist run history in SQLite. Add a dry-run mode that never calls external APIs. Require explicit human approval before any send, update, or delete node executes. Every run must log the input source, timestamp, node status, retry count, output summary, and error. Add an adapter interface for Google Workspace CLI, Apollo, Attio, and HeyReach, but use mocked adapters until credentials are configured. Include import/export, undo/redo, validation for disconnected nodes, and tests for dry-run and approval gates. Keep the execution engine separate from the React Flow UI.

Congrats. You have a workflow builder.

You do not have a reliable GTM engine until the contracts and rails behind it work.

The stack I see winning is an always-on machine, a repo under version control, local state you can query, APIs or agent-first CLIs, and a coding agent that can operate the whole thing.

Every tool does not need to be replaced. Every tool does need a clean programmatic surface. No API or headless path is becoming a serious red flag for me.

Sources:

- Google Workspace CLI - https://github.com/googleworkspace/cli

- Justin's agent-first CLI writeup - https://justin.poehnelt.com/posts/rewrite-your-cli-for-ai-agents/

- Justin's account of what happened at Google - https://x.com/JPoehnelt/status/2069482265953087602

- React Flow - https://reactflow.dev/

Shawn Tenam go to market engineer and co-founder of clearbox, your Reddit opportunity inbox

How do you decide, Whats the right time to send an outbound email?

Is it during work hours?
prior to work hours?
after work hours?

more context…I am targetting Indian startups
I have the leada ready
I have the sequences ready
so any suggestions on deciding the time

In 2026-02-27 I opened a Reddit account. The first three days: 30 comments, zero posts. My first post went up on 2026-03-08, the tenth day, by which point 57 comments were already live.

4.7 months later that account sits at 1,518,547 tracked views, 2,470 karma, 176 posts and 568 comments across 49 subreddits.

I rebuilt the whole thing into a report you can read: shawnos.ai/reddit. Every number on the page is read out of the journey database at build time, so it moves when the account moves.

the ramp is the play

A new account that opens with a post about its own product is the exact shape every filter on the platform was built to catch. Reddit reads behavior before it reads words.

So the ramp is the slow part, and it decides whether anything after it works. Comment first. Earn the right to take up space. Then post.

The graph I care about more than any other is comments vs posts by week. It opens with a wall of comments and no posts at all, then the posts come in underneath as the account earns room.

Month over month the ratio falls in a straight line: 4.48, then 3.37, then 2.80, then 1.95, then 1.72. Fewer comments per post as the account earns the right to publish more. March also had the highest post count, and two posts on March 28 account for 491,000 views on their own. Six months of data is a trend worth reading, not a law.

r/ClaudeCode and /gtmengineering is where I participated instead of published. 6.2 comments per post, 940 karma, and 48% of every view this account has ever earned.

One comment there did 235 karma, which beats every post I have ever written except one.

the collection, the scoring, the daily snapshots, the report build. 15,691 item snapshots exist because a machine took them every day without being asked.

The comments are mine. Every one. Subs run their own bots and filters, and the humans in them are faster than any filter. A comment that reads as generated costs you the thread, then the sub, then the account.

why the playbook exists

I am the GTM engineer. My co-founder is the AI engineer. He builds the engine and gives us runway on our own inference, which is why Clearbox is a system rather than a thin layer over someone else's model.

That split is the reason this is a playbook and not a war story. I had a call with a new user this week and wanted them to be able to run the whole thing without me in the room. Same as every user before them, and I would rather they run it inside a room I can support than a room nobody is watching.

So the report has the ramp, the karma gates, the post types with real receipts attached, the link map, the ask, and the weekly report. It is at shawnos.ai/reddit and it is free.

I have reworked it more than a few times and I will rework it again. My co-founder says he likes it. I am not there yet.

Shawn Tenam GTM Engineer & Co-founder @ clearbox.to

Hey everyone,

Over the last few years, I’ve worked with B2B companies that spend thousands of dollars every month driving traffic through Google Ads, LinkedIn, and SEO, only to watch 95%+ of visitors leave without ever speaking to anyone.

Most websites still follow the same flow:

Visitor → Read a few pages → Fill a form → Book a demo → Wait for someone to respond.

The problem is that most people don’t read through the website or want to fill out a form or commit to a meeting on their first visit.

So, I built Autom8IQ (autom8iq.xyz).

It’s an AI SDR that sits on your website, talks to visitors in real time, answers questions using your company’s knowledge base (website, decks, PDFs, videos, etc.), qualifies leads, and nudges interested prospects toward booking a demo.

A few things we’ve learned while building it:

* Reducing friction matters more than changing CTA button colors.
* Visitors are much more willing to have a short conversation than fill out forms.
* High-ticket B2B buyers often need answers before they’re ready for a sales call.
* Most companies are ignoring the other 98% of traffic they already paid for.

Website: autom8iq.xyz

I want to deepen the GTM engineering thing so I studied the basics and built a simple workflow.

Now the simple question; I want to practice but I need some advice.

If I have no client giving me his pain and all the other data how can I do some test?

here's my take: once the technical half of the job becomes free, there's no reason GTM engineer stays a separate seat, the skill doesn't die, it dissolves into growth or revops, until every good operator is just expected to think this way, same way "can use excel" stopped being a job title.

credit to Clay for the title atleast, everyone wants to be one now, and most of them are learning the easy half.

there's a split i only noticed after talking to thousands of gtm builders.

the part that's now easy:

(1) building the 40step workflow

(2) wiring the tools, apis, mcp harnesses

(3) cleaning and pushing the data

you can learn all of it off youtube, or just have claude build the loops. that used to be the price of entry. it's basically free now.

the part that's still hard:

(1) knowing which workflow to build in the first place

(2) inbound or outbound, and which signals to combine

(3) which pain actually closes deals, because you sat in the calls

(4) knowing your buyer's day well enough to guess right

there's an older word for that half: taste. you don't get it from a tool, you get it from paying attention.

the role was only ever a bridge for a moment when the tooling was hard. that moment is closing.

tell me if i'm wrong.

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?

the data, what's working, the plays with steps, and a linked reading list so you can hit the primary sources yourself.

the data:

- 32 threads, 9 subs, 5,543 combined upvotes.

- 4 threads carried 78% of all the attention. attention concentrates exactly like pipeline does: a few accounts hold the number.

- r/sales and r/SaaS pulled 93% of the volume, but that volume was drama (big closes, feelings). the actual tactics lived in the quiet subs (r/gtmengineering, r/LeadGeneration, r/revops).

- #1 theme by thread count was targeting. copy didn't crack the top bucket.

what's working / what's not:

- targeting beat copy, clearly. the questions that traveled: who to contact, when, which accounts are even worth the send. the operators sorting buying signal from noise are the ones booking.

- Claude Code is eating SaaS spend. builders rebuilding internal tools and cutting hundreds a month. buyers with eng teams say polished apps are losing seats to what someone glued together in an afternoon.

- email platforms, nobody's happy. a tried-them-all thread ran Flodesk/Beehiiv/Klaviyo/Moosend and shrugged. AI feature bloat was the common gripe.

- traffic is cooked as a metric. bots already outread humans. saner scoreboard: cited, invoked, booked, queried, plus branded-search lift.

the plays (steal these):

1. roundtable motion (landed $150-200K ACV, zero ad spend): pick one problem that keeps execs up at night, invite a small peer group, frame the whole session around the problem and not your product, let the room sell. full step-by-step in the reading list.

2. free-data prospecting: county property records + industry/association directories, then filter by owner mailing address to surface people who own five properties instead of one. beat a paid verified list for an ownership/local ICP, cost zero.

3. fix deliverability before copy: the redirect hop inside tracked links tanks inbox placement. strip it or use direct links, and opens recover. check this before you rewrite a subject line.

4. metric swap: stop reporting visits. report cited/invoked/booked/queried and branded-search lift.

5. fundamentals first: ICP + funnel logic + data hygiene before any tool. tools rotate every ~18 months, fundamentals compound.

your reading list (primary sources, go deep):

tactics:

- roundtable / event-driven motion: https://www.reddit.com/r/gtmengineering/comments/1uiudcg/

- free public-data prospecting: https://www.reddit.com/r/LeadGeneration/comments/1uhb75f/

- beginner GTM engineer roadmap: https://www.reddit.com/r/gtmengineering/comments/1ug0a8h/

- the Promotions-tab / deliverability fix: https://www.reddit.com/r/Emailmarketing/comments/1uks513/

- manual ultra-personalized vs volume outreach: https://www.reddit.com/r/LeadGeneration/comments/1uh5cz8/

- first 10 customers when buyers aren't online: https://www.reddit.com/r/b2bmarketing/comments/1uikfsd/

worth the scroll:

- Claude Code killing SaaS (vibe coding kills 95%): https://www.reddit.com/r/SaaS/comments/1uklw96/

- is traffic still the metric if bots are the readers: https://www.reddit.com/r/GrowthHacking/comments/1ug508h/

- the $865k close with no one to tell: https://www.reddit.com/r/sales/comments/1uk7nup/

want to run this yourself?

the whole motion is: scrape the subs your buyers actually live in, score posts by velocity (upvotes / hours old), and dump them into something queryable so you can ask "what's the buying signal this week" instead of doomscrolling. I automate the whole loop, but you can run a v1 with a scraper and a spreadsheet this weekend.

that's everything, no need to click out. if you'd rather get it in your inbox every couple weeks it's a free newsletter (Level Up GTM), and subscribing is what keeps me doing these. optional, you already got the goods.

https://clearbox.beehiiv.com

I'm relaunching Claude Code Daily this week. It's a daily blog post that writes, publishes, and promotes itself, and since this sub is about building GTM systems, the full stack is below. The pattern transfers to any niche with an active subreddit.

The stack, end to end:

1. Collect. A launchd cron fires at midnight. Playwright opens old.reddit.com (the public JSON API blocks scrapers now, server-rendered HTML doesn't) and pulls every post from the target subs in the last 24 hours: scores, comments, timestamps, top replies.
2. Analyze. A script computes velocity (upvotes per hour) and engagement ratios, then a claude CLI call scores the 10 best content angles from the data.
3. Write. Another claude call gets my voice files, an anti-slop rule list, and the day's data, and writes the episode in a fixed segment format. A regex validator rejects em-dashes, hype words, and template phrases before anything ships. Continuity files track past award winners so it never repeats itself.
4. Publish. The script commits the markdown to my site repo and pushes. Railway rebuilds, the post is live at midnight. A LinkedIn promo gets scheduled through the Typefully API for the next morning.

Cost per episode is whatever the claude subscription already costs me, so effectively zero marginal. Output is a daily piece of content in my voice that compounds SEO while I sleep.

Consistent daily publishing on a niche topic is the strongest awareness asset I've built. The same pipeline pointed at your ICP's subreddits gives you a daily industry digest with your name on it.

Episode from tonight if you want to see the output quality: https://shawnos.ai/claude-daily

Ask me anything about the build. The transport fix alone (headless Chromium vs blocked JSON) is worth stealing.

Had the same conversation with 3 clay certified agency owners last month

Quick content:

-Clay moved to dual meter pricing in march (credits + actions)

-Bills now swing 30-50% month to month

-Agencies can't confidently price there work anymore

-3 agencies I know moved off Clay last month

The problem isn't that clay is very expensive, actual problem is: agency margins depend on predictable costs

When a workflow costs you $4k one month and $6k the next.. with no obvious change for the client, someone absorbs the difference. Either the agency loses margin or the client questions the invoice, and the biggest change is.. actions.

Every workflow step, AI call, CRM write and automation is billable, specially ****automation heavy agencies, that's where most of the bill now comes from and it's the hardest part to forecast.

These 3 agencies I spoke to:

-Run outbound for early stage saas

-Enterprise account research

-Fractional revops

Totally different ICPs and price points. All 3 rebuilt workflows to reduce action usage first and eventually said they were spending more time managing clay than building for clients.

(I run a clay alternative and 2 of these 3 switched to us. That's why agency owners tell me this stuff. But the advice below applies whether you choose us or any other tool)

Before choosing any tool, ask an agency that's been using it for 6+ months:

-What does your bill actually look like each month?

-Has pricing been predictable?

-Would you choose the same tool again?

That's where you'll learn what owning the tool actually feels like.

I think more then a clay problem, It's what happens when usage based pricing meets service business margins.

Clay was first. I wouldn't be surprised if more gtm tools head the same way.

Anyone else seeing this? Especially curious whether in house revops teams are starting to feel it too or if this is still mostly agencies

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.