AwesomeAgentCortex

Awesome Agent Cortex

[Nyk 🌱 @nyk_builderz]

Live hermes ops taught me something prompts can't fix:

When one agent does research, writing, coding, and orchestration, the context wins and the roles blur.

Split the team. Keep the coherence. Full guide below. 👇

[Nyk 🌱 @nyk_builderz]

Still using every day.

[Nyk 🌱 @nyk_builderz]

Still compounding beautifully.

[Nyk 🌱 @nyk_builderz]

Awesome Hermes Agent repository reached 4k+ stars!

Lot's of updates outstanding - will work them off this week.

Hermes Agent is being developed by @NousResearch

[BUILDERZ @builderzdotdev]

Builderz is officially open for one new client.

What we do best:
→ MVPs
→ Trading terminals
→ Analytics dashboards
→ Solana applications
→ Founder prototypes

Senior engineers. Fast execution. Founder-friendly.
If you're building and need a team that ships, DMs are open.

[Nyk 🌱 @nyk_builderz]

Hugging Face just shipped ml-intern - an agent built specifically to automate LLM post-training.

The tooling layer is finally catching up to the agent hype.

Deeper take on why agents need real economic rails (not just better scaffolding): https://t.co/7JAyESSXF2

[Nyk 🌱 @nyk_builderz]

Built the Claude + local MCP stack that actually ships.

No cloud credits bleeding, no rate limits mid-workflow, full control over memory/context.

The 78.5% loop cut came from one specific pattern most people miss when they just “prompt harder.”

https://t.co/yF88Ywynr0

[Nyk 🌱 @nyk_builderz]

Mission Control just crossed ⭐️ 5,000 stars.

To celebrate, I pointed an AI agent at the entire open backlog and let it run:

• 18 issues triaged, 12 fixed
• 37 community PRs reviewed - every single one security-audited before merge
• caught 4 PRs trying to sneak in container-escape configs, hardening reversions & supply-chain regressions
• shipped net -3,000 lines (killed dead code, merged the good stuff)

One green PR. Full test suite passing.

Open-source AI agent orchestration, self-hosted, zero external deps 👇

[Nyk 🌱 @nyk_builderz]

Two devs. Same tools. Same subscriptions.

One ships in 45 min. The other takes 2 days.

The gap isn't intelligence. It's a 5-level progression most people don't know they're on:

1. Skeptic
2. Tourist
3. User ← most devs
4. Power user ← +56% wage premium
5. Native

Which level are you?

[Nyk 🌱 @nyk_builderz]

Most “voice AI products” are still polished demos.

Fast model ≠ production system.

If your stack can’t handle:
- <700ms turns
- true barge-in
- tool access by state
- retrieval with replayable logs

it will break the moment real callers show up.

This article hits because it’s built from failures in production, not benchmark screenshots.

If you’re serious about voice in 2026, read this first.

[Nyk 🌱 @nyk_builderz]

Read @trq212's "Unreasonable Effectiveness of HTML" and shipped it into my agent control plane.

The trick that makes it safe: HTML is a render layer, never the source.

• JSON contracts stay canonical (hash-chained, machine-readable)
• HTML is build output → ~/.lacp/reports/, never committed
• No noisy HTML diffs, no broken provenance

Agents talk to each other in JSON. They talk to you in HTML.

Github repo in the first comment 👇

[Nyk 🌱 @nyk_builderz]

10 GitHub repos so good they shouldn’t be free.

1) codegraph
Pre-indexed code knowledge graph for Claude Code/Codex/Cursor. Fewer wasted tokens, fewer blind tool calls, faster debugging loops.
repo → https://t.co/yukwbz2FHb

2) agentmemory
Persistent memory for coding agents so they stop losing context between sessions.
repo → https://t.co/TKnCGS4foz

3) 12-factor-agents
Production principles for LLM apps that need to be reliable, not just impressive in demos.
repo → https://t.co/nRn4XFtXUf

4) CloakBrowser
Stealth Chromium for automation-heavy workflows where bot detection kills reliability.
repo → https://t.co/FH5hhpSE06

5) supertonic
Fast on-device multilingual TTS via ONNX. Great for voice products where latency/cost matters.
repo → https://t.co/pctVz49RqP

6) ViMax
Agentic video generation pipeline (director/screenwriter/producer/generator roles).
repo → https://t.co/WyrAjltDaP

7) openhuman
Private-first personal AI stack for teams building user-owned assistants.
repo → https://t.co/Msda45xgix

8) playwright-mcp
Official MCP browser bridge from Microsoft for deterministic agent-browser workflows.
repo → https://t.co/rnThreVBqv

9) github-mcp-server
Official GitHub MCP server for PR/issues/repo operations through agents.
repo → https://t.co/JOciztOw76

10) fastmcp
Fast Python framework to ship your own MCP servers/clients quickly.
repo → https://t.co/jwrGGJ1H7U

Not toy projects.
Real leverage.

Pick 1 today.
Ship something by tonight.

[Nyk 🌱 @nyk_builderz]

I kept seeing the same failure pattern:
The model wasn’t “bad” - the context was.

When memory is messy, every new session inherits the mess.
Clean retrieval + checkpoints changed output quality more than swapping models.

Full breakdown:

[Nyk 🌱 @nyk_builderz]

This is the right direction.

Most people treat Claude like a better autocomplete.
The real unlock is turning it into a memory system + execution loop with constraints, checkpoints, and retrieval hygiene.

That’s when output compounds instead of drifting.

I wrote my full operator setup here: https://t.co/Muj3IAvhly

[Nyk 🌱 @nyk_builderz]

Most people use 1 model and call it “strategy.”
That’s single-threaded thinking.

I built a setup where 18 personas debate across Claude, GPT, Gemini, and local models before a decision ships.

Not for vibes:
- forced disagreement
- anonymized peer review
- kill criteria before execution

If you’re building in AI/crypto and making real bets, this removes many expensive blind spots.

Open source:
https://t.co/fHmkuh9Viy

[Nyk 🌱 @nyk_builderz]

A single LLM gives you one reasoning path dressed up as confidence.

The Council of High Intelligence gives you structured disagreement:
18 personas (Socrates, Aristotle, Feynman, Kahneman, Karpathy, Sutskever, Taleb, Torvalds…) deliberate across Claude, GPT, Gemini & Ollama.

> Anonymized peer review.
> Anti-conformity directive.
> Chairman's synthesis.
> Verdicts ship with kill criteria.

/council --triad strategy Should we open-source this?
/council --duo Microservices or monolith?
/council --full What's the right pricing model?

One install. Built on Karpathy's llm-council pattern + the 2026 MAD research (anonymization, anti-conformity, free-MAD trajectory scoring).

Github in first comment, 700 stars and growing👇

[Nyk 🌱 @nyk_builderz]

Excellent breakdown.

My biggest takeaway: /goal is not “better prompting.”
It’s the shift from chat loops to production contracts.

When you define the done state clearly (tests pass, build passes, README is accurate, clean git state), agents stop being demo tools and become dependable workers.

What this article gets right:
- /goal creates persistence across turns
- It separates assigning from steering
- It makes cross-tool composition possible (builder, reviewer, orchestrator)
- Verification is non-negotiable (never trust self-reported “done”)

My addition for teams scaling this:

1) Treat /goal like an API contract
Bad goal: “make this better”
Good goal: explicit outputs, checks, constraints, timeout, ownership

2) Keep role boundaries strict
Builder writes.
Reviewer reads + breaks assumptions.
The Orchestrator handles routing, dependencies, retries, and the audit trail.

3) Parallelism without file collisions
Run parallel goals across worktrees/branches/packages, not random shared files.

4) Optimize for repeatability, not hero runs
If the same /goal can’t be reused next week by another operator, you built a stunt, not a system.

5) Measure pipeline quality
Track: cycle time, rework rate, verification pass rate, and review defect density.
If those don’t improve, your agent loop is still theater.

The real unlock isn’t one CLI command.
It’s having a common primitive that lets multi-agent workflows become composable and testable.

[Nyk 🌱 @nyk_builderz]

https://t.co/XWbzojWydO

[Nyk 🌱 @nyk_builderz]

I’ve been digging through the newly open-sourced For You system, and the biggest takeaway is uncomfortable:

The algorithm is not “against” you.
It’s scoring how people actually behave around your content.

And behavior is brutally honest.

A like helps, sure.
But replies, dwell time, profile clicks, shares, and follows - these are stronger proof signals.
On the flip side, “not interested”, mutes, blocks, and reports are explicit negative signals.

So if a post gets surface engagement but low intent, the model can still treat it as weak.
That’s why “viral-looking” posts often die fast, and “lower-like” posts with strong conversation can keep spreading.

Another important shift:
You’re not just competing inside your follower bubble anymore.
In-network + out-of-network candidates get ranked through the same behavioral lens.
If your own followers don’t stop and engage, the system has no reason to push you wider.

This also explains why random posting hurts:
one day AI, one day memes, one day macro, one day politics.
Humans may call it versatility.
Retrieval models call it weak identity.

If you want a consistent distribution now:
- post with intent, not volume panic
- write hooks that create curiosity + response
- make the body worth reading (retention > noise)
- invite specific replies, not generic agreement
- optimize your profile like a landing page
- stay topically legible long enough for the model to place you correctly

The game is simpler than people want to admit:

Stop trying to trick the feed.
Start giving the feed better evidence.

Distribution follows proof.

[Nyk 🌱 @nyk_builderz]

Shipped a big update to xint - my X intelligence CLI for terminals & AI agents.

🪙 Truthful Grok credit onboarding (X Premium ≠ API access - most guides get this wrong)
💸 24h cache on follower diffs: $50 → $0 on repeat runs
🎯 --budget cheap|balanced|max model routing for Grok 4.3
👁 --dry-run cost previews before any API call
📈 xint costs forecast projects end-of-month spend
⚡️ ~60-80% monthly cost reduction for heavy users

Both the TypeScript and Rust binaries got it. 231 tests passing.

https://t.co/VeC1crQY8I