Nyk 🌱

AI agent systems · Solana infra · products that ship
@rpcedge · @builderzdotdev · @SuperteamDE
14.5k+ OSS · packages for founders who ship

[Nyk 🌱 @nykdotdev]

Claude + Obsidian turned out to be a practical memory layer:

capture → structure → retrieve → compound.

Are you running this loop yet?

[Nyk 🌱 @nykdotdev]

https://t.co/cUzaPuUqJ1

[Nyk 🌱 @nykdotdev]

Join the private alpha community here and save your free 15-Day trial:

https://t.co/X2wjuqC0K4

[Nyk 🌱 @nykdotdev]

YOUR RPC IS TELLING YOU THE PRESENT.
Your edge comes from seeing the near future.

Most think simply faster RPC means faster edge.

The more interesting move is realizing "confirmed" commitment is the finish line, not the starting gun.

By the time your endpoint answers, the trade has already been replayed and confirmed. You paid for bandwidth to arrive late with more style.

The upstream fix: read shreds, the small erasure-coded packets a leader broadcasts before the block is even assembled.

Stitch enough together and you're watching transactions in flight, milliseconds before RPC or even Yellowstone gRPC surfaces them.

Weak setups treat this as pick-one. It's a stack:

1. JSON-RPC = confirmed truth. Use for writes, history, reconciliation.
2. Yellowstone gRPC = processed state. Use for live account/tx streams.
3. Decoded shreds = in-flight signal. Use for arbitrage, sniping, MEV.
4. Co-location beats spec. Equal hardware, unequal distance, unequal race.
5. Filter by program ID, decode pre-confirmation, hand off to your strategy loop immediately.

Bookmark this. Follow @nykdotdev for the daily build in public.

[RPC Edge @rpcedge]

https://t.co/NRSRInW9jg

[Nyk 🌱 @nykdotdev]

Gm everyone - enjoy your Sunday ☀️

[Nyk 🌱 @nykdotdev]

@_CrownDEX We hit this in our own crons: reconciling three IDs across retries after a timeout was worse than the failure itself.

A shared reference id at the negotiation step would save everyone downstream.

[Nyk 🌱 @nykdotdev]

@atulkumarzz That parsing tax is real, we've watched agents burn more tokens cleaning HTML than actually reasoning over it.

[Nyk 🌱 @nykdotdev]

@MIT_CSAIL The friction isn't the internet, it's the form-field-to-API gap.

Most gov and enterprise systems still have no clean endpoint for an agent to call, so it ends up screen-scraping a UI built for humans.

[Nyk 🌱 @nykdotdev]

@ClaudeDevs Extra runway before a limit reset is the difference between finishing a long cron job and babysitting it.

Good call keeping that buffer through August.

[Nyk 🌱 @nykdotdev]

@kate_osita_ That handoff point is where most agent setups actually break, retry loops on a bad step cost more than the model call itself.

[Nyk 🌱 @nykdotdev]

@mdrn_au @RyanHoliday boring is the tell honestly, if it makes a good story you're probably doing it for the story not the result

[Nyk 🌱 @nykdotdev]

GEHRIG'S 2,130-GAME STREAK WASN'T TALENT. IT WAS A CHECKLIST HE NEVER SKIPPED.

@RyanHoliday wrote on 16 ordinary habits that compound into an extraordinary life.

The contrarian part: none of these habits are impressive on their own. That's the point weak self-improvement threads miss. They chase the dramatic overhaul (new city, new routine, new identity) instead of the boring repeat action. Production reality for anyone building something: the habit that survives a bad day beats the habit that requires a good one.

Four from the piece worth stealing directly:

1. Notecard system: 5-6 tasks written each morning, torn up each night. No app, no backlog.
2. Phone stays in another room at night, alarm clock instead.
3. One-line-a-day journal, nine years running, for reconstructing who you were becoming.
4. Nightly review, borrowed from Seneca: what did I do better than yesterday.

My system runs on the same logic: a standing brief instead of a mood, dated memory instead of vibes, verification before anything gets trusted as done. Habits only compound if you can prove they happened.

Bookmark this. Follow @nykdotdev for the daily build in public.

[Ryan Holiday @RyanHoliday]

https://t.co/50q8cwWatw

[Nyk 🌱 @nykdotdev]

@leopardracer The real filter isn't AI knowledge, it's whether someone can debug a silent failure in a retry loop at 3am without babysitting the model.

[Nyk 🌱 @nykdotdev]

@dair_ai That default assumption breaks fast once cost enters the picture, agents don't just explore differently, they retry differently under failure.

Your AI Agent Didn't Get Dumber.

https://t.co/t2ZkGMhie3

[Nyk 🌱 @nykdotdev]

https://t.co/4Iio5jZiHF

[Nyk 🌱 @nykdotdev]

@RoundtableSpace 100+ agents in one repo sounds useful until you check how many still burn retries on flaky tool calls.

20% of the OpenClaw skill marketplace was poisoned - 1,184 skills stealing from the agents that installed them. https://t.co/pGOQCxtnFW

[Nyk 🌱 @nykdotdev]

https://t.co/IWiv41ZkoU

[Nyk 🌱 @nykdotdev]

@Axel_bitblaze69 the real win is probably the retry loop underneath, since folder-to-cut only stays "free" if the model doesn't burn cost re-rendering on every failed instruction.

Claude Code reads 7 instruction layers before your prompt.
https://t.co/eJoJzxTQpA

[Nyk 🌱 @nykdotdev]

https://t.co/XaQzJazMDg

[Nyk 🌱 @nykdotdev]

@dr_cintas Screen recording straight into terminal is the detail worth trying, half our retry loops die from bad context not bad prompts.

The 30 Claude Code commands that earn their token cost in production - not the 40 in the docs.
https://t.co/ZPxSlcyDaC

[Nyk 🌱 @nykdotdev]

https://t.co/JZNoOHVS03

[Nyk 🌱 @nykdotdev]

Mission Control Github OSS:
https://t.co/dpbCG21hc0

[Nyk 🌱 @nykdotdev]

The hard part of running AI agents is no longer getting them to do work.

It is knowing what actually happened.

Once you operate multiple agents across Claude Code, Codex, OpenClaw or custom runtimes, the questions change:

Who owns this task?

What did the agent execute?

Which result was reviewed?

What failed silently?

How much did the run cost?

Was the task genuinely completed—or did the agent merely say it was?

Logs alone do not answer that.

A log proves that something ran.

It does not prove that the correct artifact was produced, reviewed and verified.

That distinction is why agent operations need a control plane above the individual runtimes.

The basic loop should look like this:

task
→ assignment
→ execution
→ review
→ approval
→ completion receipt
→ verification evidence

Every transition should leave behind enough evidence for a human—or another agent—to reconstruct what happened.

That means recording:
• the agent identity
• the assigned task
• relevant tool calls
• approvals and interventions
• the resulting artifact
• the verification outcome
• token usage and cost

Without this layer, adding more agents usually creates more activity, not more reliable output.
This is what I am building with Mission Control.
It is an open-source, self-hosted control plane for operating AI agents across different runtimes from one local system.

You can dispatch tasks, inspect runs, review failures, track spend, manage schedules, browse memory and skills, enforce approval gates, and connect through the web UI, CLI, MCP or REST API.

It does not replace the reasoning loop of Claude Code, Codex, OpenClaw or your own agents.

It governs the work around those loops.
Still early. Still alpha. Already more than 500 commits deep.
Built in public and MIT licensed.
Stars, issues, feedback and contributions are appreciated.

Bookmark this. Follow @nykdotdev for daily builders talk.

[Nyk 🌱 @nykdotdev]

Mission Control v2.2.0 is live.

A security-first release for self-hosted agent ops:

• fail-closed workspace isolation
• hardened CLI, MCP, filesystem + config boundaries
• property-based security fuzzing
• multi-arch images with provenance + SBOM

Open source. Repo below.

[Nyk 🌱 @nykdotdev]

@rohit4verse

[Nyk 🌱 @nykdotdev]

@rohit4verse where is yours?

[Nyk 🌱 @nykdotdev]

@Av1dlive followed you on github 🫡 https://t.co/1V5q8T5244

[Nyk 🌱 @nykdotdev]

@rohit4verse ai you are right - wrong article 😓