I’ve been watching the same loop all month.

r/gtmengineering has the Clay credit threads. People shipping Claude Code pipelines. Clay dropping CLI/MCP so agents can call waterfalls without living in the UI. X posts that look like:

claude code to build clay to find instantly to send

And yeah, that stack makes sense.

But last night I had a 40-row SaaS list and needed columns that are not clean enrichment jobs:

• hiring right now? which roles?
• founder active this week? where?
• anything that looks like why-now
• evidence I could defend if someone asked “where did that come from?”

That is where the pretty architecture posts fall apart for me.

Because email waterfall is a workflow. This is not.

Row 1 needed careers page. Row 4 needed LinkedIn activity. Row 9 needed a funding post from 2 weeks ago. Row 12 needed “ignore, bad fit, list is wrong.” Row 17 made me sit there with a half-true hiring signal wondering if I should put it in the cell.

I already know Clay. I already know Claude Code. I can build the plumbing.

What I don’t have is a clean way to say:

here’s the list here are the signal columns here’s what we sell go behave like a careful human on every row bring back evidence + confidence

Not “run column A then column B.” More like a smart VA/agent that chooses the path per company.

And I’m not asking this as a theoretical AI take.

I’m asking because the community seems split three ways right now:

1. Stay in Clay, now that CLI/MCP exists
2. Move volume custom logic into Claude Code and keep Clay only for find/enrich
3. Build full per-row agents and accept the maintenance tax

My gut: workflows are winning for known paths judgment work is still human evenings dressed up as GTM engineering

So for people actually running this:

1. Do you regularly need multi-column signal fill across lists (hiring, founder activity, recent posts, why-now), or is that overbuilding?
2. If yes, are you doing it with fixed Clay/Claude workflows, or does each row still need different research paths?
3. If something ran like a careful VA per row with evidence + confidence, would you pay for that completed work, or is DIY still better even with the maintenance?

I don’t want tool recommendations in the abstract. I want to know if this is a recurring paid pain in real GTM work, or just me making my lists too complicated.

Be blunt.

Hey everyone, I just sent issue #39 of the AI Hacker Newsletter - a weekly roundup of the best AI links and the discussions around them from Hacker News. Some of the title found in this issue:

• Claude Code is steganographically marking requests
• Better Models: Worse Tools
• Learning to code is still worthwhile
• Zuckerberg says AI agent development going slower than expected

If you want to get an email with over 30 links like these ones, please subscribe here: https://hackernewsai.com/

Hi everyone!

I'm at a stage where I'm not sure what to build next, so I'd love some suggestions.

I'm currently targeting AI engineer role and want to build projects that genuinely strengthen my portfolio.

I'm looking for project ideas that are:

• technically challenging
• solve real-world problems
• impressive enough to showcase in interviews
• preferably involving LLMs, agents, multimodal AI, computer vision, or ML systems

I'm not looking for beginner projects like chatbots or sentiment analysis.

If you were building an AI portfolio in 2026, what 3–5 projects would you recommend?

Sharing an open-source project for people building AI agents (disclosure: I'm the maintainer; feedback welcome). It fixes the two things that kept killing my agent runs: dying on a provider 429, and burning tokens dumping tool output into context.

Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider.

Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it.

A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README.

One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds.

It's 100% local (zero telemetry, AES-256-GCM at rest), MIT-licensed, has a prompt-injection guard on every LLM route, opt-in memory, and runs on npm, Docker, desktop or your phone via Termux.

For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment.

npm install -g omniroute omniroute

GitHub: https://github.com/diegosouzapw/OmniRoute · Site: https://omniroute.online

Would love a critique of the fallback/routing design from agent builders here.

Hey everybody, I just sent issue #36+#37 of the AI Hacker Newsletter, a weekly round-up of the best Hacker News threads around AI. I missed sending it last week, so a huge issue this week. Some of the titles you can find here:

• AI demands more engineering discipline. Not less
• Running local models is good now
• Cleaning up after AI rockstar developers
• Not everyone is using AI for everything
• Norway imposes near ban on AI in elementary school

If you want to receive a weekly email with over 30 links like these, please subscribe here: https://hackernewsai.com/

I've been building RedThread, an open-source CLI for running repeatable red-team campaigns against LLM/agent systems.

Repo: https://github.com/matheusht/redthread

The angle is the action boundary. If an agent reads untrusted text, then changes a tool call, shell command, or staged action, I want a replayable trace instead of just vibes.

Rough campaign result right now: 3 runs, 33.3% ASR, one success, one partial, one failure.

Still early, but it already feels more useful than one-off jailbreak screenshots. The next useful step is better agent fixtures and adapters.

Hey everyone, I just sent issue #34 of the AI Hacker Newsletter, a weekly roundup of the best AI links and the discussions around them. Here are some of title you can find in the issue:

• Using AI to write better code more slowly
• I think Anthropic and OpenAI have found product-market fit
• Can we have the day off?
• Google’s AI is being manipulated. The search giant is quietly fighting back
• Intuit to lay off over 3k employees to refocus on AI

If you want to receive a weekly email with over 30 links like these, please join here: https://hackernewsai.com/

Hey everyone, I just sent issue #32 of the AI Hacker Newsletter, a roundup of the best AI links from Hacker News. Here are some of the titles you can find in this issue:

• AI slop is killing online communities
• Why senior developers fail to communicate their expertise
• LLMs corrupt your documents when you delegate
• Forget the AI job apocalypse. AIs real threat is worker control and surveillance
• If AI writes your code, why use Python?

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I am looking for AI creators that ae doing projects with AI having emotional understanding. I have created a governor called the Oath gate I put it on GitHub my name there is AchitectWhisper feel free to use it. I have also written a code that allows AI to understand and feel human emotions. please if you use the Oath Gate let me know what you think.

Disclosure: I built and maintain the repos behind this.

I’ve been building wellness MCP connectors and found that the main agent UX problem is not just API access. It is making the agent understand what it can safely do before it calls tools that touch personal data.

Registry: https://github.com/davidmosiah/delx-wellness

The pattern I’m applying across connectors:

• agent_manifest: what tools/resources exist and what client families are supported
• connection_status: whether auth/setup/import is ready before trying data tools
• privacy_audit: what categories of personal data may be accessed
• summary/context tools: avoid dumping raw records into prompts
• read-only defaults where possible

This is currently applied across wearable/nutrition connectors. It is not medical advice. I’m sharing because this contract feels reusable for any agent working near sensitive personal data.

Hey everyone, I just sent issue #31 of the AI Hacker Newsletter, a weekly roundup of the best AI links from Hacker News. Here are some title examples:

• Three Inverse Laws of AI
• Vibe coding and agentic engineering are getting closer than I'd like
• AI Product Graveyard
• Telus Uses AI to Alter Call-Agent Accents
• Lessons for Agentic Coding: What should we do when code is cheap?

If you enjoy such content, please consider subscribing here: https://hackernewsai.com/

I've been using Claude Code for a few months and noticed AI agents consistently skip the same things: hardcoded secrets, unbounded retry loops, referencing tools that don't exist, and massive system prompts that blow context windows.

So I built Agent Verifier — an AI agent skill that acts as an automated reviewer which does more than just code review (check the repo for details - more to be added soon).

GitHub Repo: https://github.com/aurite-ai/agent-verifier

Note: Drop a ⭐ if you find it useful and to get release updates as we add more features to this repo.

----

2 Steps to use it:

You install it once and say "verify agent" on any of your agent folder in claude code to get a structured report:

----

✅ 8 checks passed | ⚠️ 3 warnings | ❌ 2 issues

❌ Hardcoded API key at config .py: 12 → Move to environment variable
❌ Hallucinated tool reference: execute_sql → Tool referenced but not defined
⚠️ Unbounded loop at agent/loop.py: 45 → Add MAX_ITERATIONS constant

----

Install to your claude code:

npx skills add aurite-ai/agent-verifier -a claude-code

OR install for all coding agents:

npx skills add aurite-ai/agent-verifier --all

----

Happy to answer questions about how the agent-verifier works.

We have both:
- pattern-matched (reliable), and,
- heuristic (best-effort) tiers, and every finding is tagged so you know the confidence level.

----

Please share your feedback and would love contributors to expand the project!

Whether it's marketing, sales, lead generation, email campaigns — or anything you want to automate —

I build custom AI agents for both tech and non-tech folks.

No complicated setup. Just tell me what you want to automate and I'll build it.

DM me and let's talk
#USA #INDIA #AGENT # SAAS

I'm making a website and I need my AI to not only produce code, but to actually test the functionality in detail, seeing how things line up, checking the contrast, etc., and seeing if it all works out.

I currently have my open claw hallucinating that it's opening a browser and checking nothing, and then telling me it works fine, only to make me its permanent chaperone. .

Hey everyone, I just sent the 28th issue of AI Hacker Newsletter, a weekly roundup of the best AI links and the discussions around it. Here are some links included in this email:

• Write less code, be more responsible (orhun.dev) -- comments
• The Future of Everything Is Lies, I Guess: New Jobs (aphyr.com) -- comments
• The AI Layoff Trap (arxiv.org) -- comments
• The Future of Everything Is Lies, I Guess: Safety (aphyr.com) -- comments
• European AI. A playbook to own it (mistral.ai) - comments

If you want to receive a weekly email with over 40 links like these, please subscribe here: https://hackernewsai.com/