KiloClaw launched on Product Hunt last February (ranked #1 Product of the Day, #1 Product of the Week) and the team just started a new series, introducing ClawBytes, a cookbook of ready-to-use automation recipes for @KiloClaw and @OpenClaw.

Let's get cooking.

Something I keep coming back to: if an auditor asked your team to prove that the AI-generated code in your last release passed your risk policy what would you actually hand them?

Most teams I talk to would produce a PR link, maybe a Copilot usage dashboard, and a verbal description of their review process. No record of which model generated which function, no risk score at insertion time, no machine-verifiable proof that a human reviewed the AI context rather than just the diff.

We just shipped an indemnity certificate system in LineageLens. You define a policy (max risk score, allowed models, require human review, license clean), call an endpoint for a specific PR or release, and the system evaluates every provenance record tagged to that scope against your rules. Either it issues a signed Ed25519 certificate verifiable by any third party without workspace credentials or it returns a structured list of exactly what failed and why.

The certificate anchors to the provenance hash chain at the moment of issuance, so it's not just "we said it was reviewed." It's a cryptographic statement tied to the capture records that existed at the time.

Changes:

- Added root locations path to address bar for easy drive / location switching, especially useful in split-view mode

- Locations path can be added to favorites and tagged

My friend launched his tool a few weeks back and texted me at like 7am saying he was already awake refreshing the page. I checked in around noon and he hadn't eaten, hadn't left his desk, just kept switching between the upvote count, the comments, and Twitter mentions on repeat.

At one point the ranking dropped two spots and he called me convinced something had gone wrong, even though the actual upvote count was still going up. I tried telling him the number going down for a second didn't mean much, but it didn't really land in the moment.

What got me was that he barely replied to half the comments people left, because he was too busy watching the count instead of talking to the people leaving it. Told me afterward that the conversations he did have were the part that actually mattered, way more than where he landed on the board, but he only realized that after the day was basically over.

I haven't launched anything myself yet, so I don't know if this is just how day one always goes or if he made it harder on himself than it needed to be.
For people who've actually done this, is the refreshing and spiraling pretty universal, or is there something you did differently that kept you sane?
And for anyone else who hasn't launched yet either, does hearing this make you more nervous about your own launch day, or does knowing it happens to almost everyone take some of the pressure off?

I got feedback from a PingPrompt user that stuck with me.

He said PingPrompt helps make prompts stronger, richer and more focused.

Two new built-in AI Reviewers ship: a glossary reviewer and an instructions reviewer. Toggle on the glossary reviewer and every configured glossary term is automatically checked on every translation. Toggle on the instructions reviewer and every configured instruction is checked the same way. Open any translation in the dashboard log and see pass/fail per term, pass/fail per instruction, with the reviewer's reasoning on failure. One toggle replaces authoring an AI Reviewer for every single criterion.

Also shipped

• Per-engine AI Reviewer assignment. Attach different reviewers to different engines instead of one set for the org.

• Organization-level timezone setting. Reports display in the timezone you configure for your org.

Full changelog: lingo.dev/changelog

Hey everyone

Netfox 0.8.0 is out, and it ships something that came straight from this community: a Prometheus-compatible metrics endpoint.

SWIFT just built its answer to stablecoins, and almost everyone misread it.
Seventeen banks, six continents, live this month on a tokenized-deposit ledger. The story got told as "banks embrace blockchain." It's really banks defending their deposits.
When a customer buys a stablecoin, the money leaves the bank for an issuer's reserve. Roughly $315B has already made that trip. Tokenized deposits give you the 24/7
programmability without the money ever leaving the balance sheet. That's the actual point.
So for any new rail, the question isn't "is it on-chain?" It's "whose balance sheet does the money sit on, and what do they lose when it moves?"
Still just a pilot, though. Settlement runs on the old rails, and stablecoins are already doing $300B+ for real.
Do tokenized deposits win corporate flows, or just fragment them further?

Audit logs are now available an append-only record of every state-changing action in your organization.

Each change to engines, glossaries, instructions, brand voices, AI reviewers, API keys, members, and roles is captured with the actor, action, target, and request context. The log lives in the dashboard and filters by user, action category, target, and date so you can see exactly who created, updated, or removed a glossary item three weeks ago, even if the actor is no longer on the team.

Also this week:

Your agents don t need more expensive models. They need to learn from their own mistakes automatically. Introducing Smart Skills: living, self-improving instructions that get generated, tested, and refined automatically based on how your agents are performing with your users in production.

How it works:

• Generated from real signals. Voker watches for user corrections, frustration, and failed outcomes, then writes a Smart Skill to fix the issue. Proven to perform better than Claude use these logs to fix .

• Self-correcting. Every Smart Skill is versioned and measured against your agent's performance. If a Smart Skill is underperforming or regressing behavior, Voker flags it and auto-optimizes it.

• Personalized. Enable Smart Skills globally, to specific companies, or to individual users.

• Tied to cost and resolution tracking. See how Smart Skills are saving tokens. Fewer complaints, faster resolutions, and less agent "thinking" usually means lower spend, and now you can prove it.

• Fully auditable. Every Smart Skill addition or removal shows up as a milestone directly on your performance charts, so you can see what changed and when.

Notion: we used it for everything, which meant it was organized for no one. Docs lived in 4 different structures depending on who created them. We moved back to Google Docs for anything collaborative.

Slack: we kept it but killed 80% of the channels. The "tools" channel, the "inspiration" channel, the "random" channel. Nobody used them except to post things that didn't need a response. Now there are 6 channels. It's quieter and somehow we're more on top of things.

ArkTabs reached 2.7k+ Users. And What is most important? The total amount spent on marketing is $0.

What do you think what is the main feature that is attracting the users?

Thank you!

We put all 111 one click apps on the first screen. It felt like the whole point of the thing, everything in one place, look how much you get.

People opened it, scrolled, and left. Not confused. Just done deciding before they'd made a single one.

A few hours left and the support today has gone further than we expected for day one. Thank you to everyone who voted and sent feedback.

If you have not jumped in yet, now is the time. And if you have already tried Spira, we would love to hear what worked and what didn't.

Today, we are taking our biggest step yet.

We just open-sourced the Solarch core engine.

Here is what this means for you:

Ten replies in on X, still at 10 followers, so I stopped guessing and pulled the actual numbers.

The spread on real replies I posted: one got 2 views under a 1,100 view post. Another got 2 views under a 5,842 view post. One got 1,204 views, replying under a post at 1.9 million views.

Every one of them landed at roughly 0.03% to 0.17% of the parent post's reach, no matter what I wrote or when I posted it. My own follower count never showed up as a variable at all.

The harder part: even the 1,204 view reply got zero likes and zero replies back. So reach was never the actual bottleneck, whatever I said in the reply was.

Hey everyone

Since the launch, we went heads-down and shipped a batch of improvements. Quick rundown:

• Imports improved - both in terms of reliability and efficiency. The big one. If you tried Unabyss early and a source synced halfway or dropped data, that's fixed. Every import runs clean. And we re able to build even more detailed context for you.

• Better cross-agent memory. We upgraded the store tool in our MCP - so if you run more than one agent, they finally share context. What you tell Claude, ChatGPT picks up too. No more re-explaining yourself to every assistant.

• New integrations - we ve added 20+ integrations (CRMs, project management tools, ads managers, and more). More sources you can pull context from. Open your Unabyss and check the connections - the new ones are there, ready to use.

• Skills, built into the MCP. Ready-made playbooks that handle the repetitive parts of your day, right inside the MCP. Both our and forks of the most popular skills that could use some context :) Worth a look - a few will probably become habits.

Feels like people are using it as a shortcut to dismiss products they don t like.

At the end of the day, devs have always relied on tools to go faster, frameworks, libraries, Stack Overflow, templates, all of it. AI is just the next step in that chain. Typing code was never the hard part anyway. Understanding what to build, making the right product calls, and handling messy edge cases is where the real work is.

AI doesn t change good vs bad builders, it just changes speed.

I've been building with tools like Claude Code, Cursor, and Replit, and they've dramatically sped up coding.

But I've noticed there are still parts of the workflow that feel surprisingly manual.

For example:

• Finding a prompt that worked a few weeks ago.

• Remembering how a specific feature was generated.

• Keeping architecture and AI context organized across different tools.

• Iterating on AI-generated code without starting over.

We're holding a top spot and this is the final sprint! If Spira looks useful to you, a vote in the next few hours counts the most.

https://www.producthunt.com/prod...

Hi PH fam!

Quick update since our Product Hunt launch - what's new in @Tamadoggo, our iOS journal for dogs and cats.

February 10, 2026 Microsoft introduced the AI Performance dashboard in Bing Webmaster Tools. It showed website owners how often their content was cited in Copilot, Bing AI summaries, and partner AI integrations. The first time a major search provider gave publishers direct, first party AI citation metrics.

July 7, 2026 Google announced platform properties in Search Console for social and video content. Five months later.

Bing's dashboard was not a fluke. It was a signal.

What Bing offered first

I spent 8 years as a VC. Invested in 47 companies, read thousands of decks, sat through hundreds of pitches.

And the whole time, from my side of the table, I kept noticing the same thing. The founders struggling to raise usually didn't have worse ideas. They were the ones who couldn't get in front of the right investors, or were emailing the wrong ones with the wrong message.

hey chaps, just launched again: https://www.producthunt.com/post...

please leave an upvote and lets spread the word for this open-source project! Aim is to have a community of builders on it!

AI pricing today doesn t fully reflect real cost.

Some reports and industry estimates suggest that real inference cost can be 5 10x higher than what companies currently charge, depending on model size, context length, and infrastructure load.

So the market is not really stable yet.
It s still in a growth phase, supported by subsidized pricing and scale.
And usage is already massive.

Most of you probably saw this report: