Agentcad now supports multiple parts in assemblies.

The @Layercode team has quietly been cooking something new: @Toyo - an AI computer in the cloud that helps founders grow their businesses with AI agents, without needing any coding or technical skills.

This new product is now generally available, and it's launching today on @Product Hunt.

SlimSnap ships a Claude Code skill that auto-loads your latest screenshot so the agent reads the structured JSON without you pasting anything. That's the "magic" version of the workflow.

For everything else (Cursor, Lovable, bolt.new, Replit AI, claude.ai), the screenshot spec works but you paste the JSON into chat manually. It works, just not as smooth as the Claude Code flow.

Where do you actually paste screenshots into AI tools today? Vote in the comments, ideally with one line on what your screenshot loop looks like there. That tells me which agent gets the next auto-loader.

Don't take this as a date promise. Just trying to figure out which one to start with after the current backlog.

Backlinks are still one of the strongest signals Google uses to judge whether a page deserves to rank . But the math changed. Google's December 2024 and October 2025 spam updates devalued link networks, paid placements, and AI-generated guest-post farms . The old volume play is dead. The new play is about quality, relevance, and context.

In the age of AI search, backlinks influence visibility differently. They act on the retrieval layer the live search index that grounds AI answers rather than the model's frozen training weights . A domain's Authority Score correlated with AI mentions at Pearson 0.65, according to a Semrush study of 1,000 domains tracked across five AI surfaces . Raw backlink volume correlated far more weakly.

Two insights from the data:

Something I have been thinking about in the AI governance space that I do not see discussed enough: provenance capture is not like most tooling categories.

With most observability or audit tooling, the reasoning is "we should have this so we're better positioned going forward." You can turn it on when the need becomes clear. You lose some history, but the tooling from that point forward is complete.

AI code provenance does not work this way.

The prompt a developer submits to Claude Code exists for a few hundred milliseconds in transit. After the model returns its response and the editor applies the change, that prompt is gone. Git records the diff. Nothing else records the origin by default. There is no reconstruct operation.

AI tools can generate a convincing web-page draft in seconds. The difficult part is usually the handoff: the headline may be too generic, the CTA may not match the real conversion goal, and the middle of the page may not explain the product clearly enough to deliver.

HtmlDrag gives teams a visual finishing workflow for HTML generated by external AI tools. Paste the raw HTML into the Paste Code entry, turn it into an editable page, then select and refine the copy, CTA, colors, visual hierarchy, and section story without asking a non-developer to work directly in source code.

Picture this.

You write a brilliant article. It ranks number one on Google. You check your traffic and it is falling anyway.

No penalty. No ranking drop. Just fewer clicks.

Welcome to marketing in 2026.

Thank you all the supported and interacted with Orus via either WhatsApp, Telegram or Claude/Chatgpt.

We created 100 new conversations with you and fine tune the personality and response speed for all the chats! What would you like to ask to an AI agent? What other platforms would you like your agent to be in?

Okay this one's fun to share.

Ever wish you could just tell your AI exactly what to remember and what to skip, instead of hoping it figures it out on its own? Just give it instructions like you would give an intern: check that folder, use that info, forget about this part.

That's basically what we built with the @aicf handle. It's a custom instruction system baked right into AI Context Flow, you just add it to your prompt and tell it exactly what context to pull.

Made a quick tutorial walking through 3 examples we actually use ourselves:

Yes, backlinks are still one of the strongest signals Google uses to judge whether a page deserves to rank. For competitive commercial queries, they remain one of the clearest ways to separate two pages with similar content.

But the math changed.

In the age of AI search, backlinks influence visibility differently. They act on the retrieval layer the live search index that grounds AI answers rather than the model's frozen training weights. That split explains why studies measuring pure ChatGPT answers sometimes find links matter less, while studies measuring AI Overviews or Perplexity find they still matter.

The shift in 2026:

Posting something we published this week for anyone here running a SaaS company or a company newsletter.

The premise: most SaaS teams ship 15 to 40 meaningful items a quarter and send their full customer list somewhere between zero and two emails explaining any of them. The changelog is complete, accurate, and visited by nobody.

The diagnostic in the post is the part worth stealing. Count the items you shipped last quarter that a customer would care about. Count the sends that told your whole list about any of them. Divide. That's your Ship-to-Tell ratio, and it's almost always a single digit. Everything under 100% is value you paid to build and then didn't distribute.

A lot us are frustrated with Clay credits usage.
It almost feels like complexity is built in to maximise credit usage- not maximise value delivered to the users.
I have spoken with 200+ founders and builders while building Jesse.
A vast majority spend more per month Clay to just find 100 leads (not convert, just find) than they spend on building their app.
This is very unfortunate situation to be in.
If you find yourself in such a situation- might as well try Jesse (#3 product of the day, and featured mention in PH weekly newsletter)> https://agentjesse.floworks.ai/
Whats more?
> Jesse does not restrict you to 102-12 data sources.
> Jesse is natural language based.
"show me soccer facilities opened in midwest post last summer" -> relevant if you want to sell your booking SaaS
"homeowners looking to rent out their homes having moved to a different city" -> great leads if you are in real estate
> Best yet? starts with just $9 plan with a generous free trial.

Aviquill's new level design tool lets you block out rooms, paths, and layouts before touching Unreal or Unity. It's early, but already makes prototyping way faster and less painful. Built for game devs who want a simple space to plan gameplay flow.

I'm also planning to expand this into a full 3D level editor later on, something lightweight, engine style, and made for quick iteration. It'll take time, but the goal is to let you sketch 3D spaces right inside Aviquill.

Two weeks ago, I hit "Publish" on my MVP. I had been working on it for nearly 3 months late nights, endless bug fixing, polishing the UI until it felt just right. In my head, launch day was going to be this big moment where traffic spikes, people leave reviews, and feedback comes pouring in.

Reality? Complete silence.

For the first 48 hours, I refreshed my analytics page every 10 minutes. 42 visitors. 0 signups. No DMs, no comments, nothing.

My first reaction was panic: "The product is garbage. The positioning is wrong. I picked the wrong stack."

But after the initial sting wore off and I talked to a few fellow builders, I realized something important: Silence isn't failure it's just raw data.

Here are 3 hard lessons I learned from that quiet week:

1. "Cool product" doesn't mean "I'll use it": I sent the link to 15 friends. They all said "looks super clean!" but none of them actually used it twice. Friends want to be polite; strangers give you the truth with their time.

2. I spent 80% of my time building features, 5% explaining the problem: My landing page explained how the app works, but completely missed why anyone should care today.

3. The post-launch gap is where most makers quit: The gap between "it works technically" and "people care" is huge. It s emotionally exhausting, but it s actually where the real building starts.

I m currently tweaking the landing page copy and testing 1-on-1 outreach instead of shouting into the void on socials.

For anyone who has been through this:

• What was your "oh shit" moment right after launching?

• How did you break through that initial wall of silence to get your first 10 active users?

Would love to hear your raw stories (and failures too, not just the success posts)!

We just shipped a big update to Easy Scribe. You already get an AI-generated step-by-step doc from a single recording. Now you can convert that same doc into an interactive walkthrough: a clickable, player-based tour your audience follows at their own pace.

Hey everyone,

We just listed Cloud World Model on the agent storefront at https://tryponcho.com/m/www.clou... - and wanted to share it here first.

Every company already has the knowledge.
It s spread across documents, help centers, PDFs, slack messages and thousands of everyday conversations.
We ve been building around that idea for a while now.
Almost time to show you where it led us.

I was rereading parts of The Mom Test, and it reminded me how easy it is to mistake politeness for validation.

Someone says the idea sounds useful, they like the direction, they would definitely try it, and maybe they even suggest a few features. It feels like progress, but sometimes they are just being nice.

The dangerous part is that polite feedback does not feel negative. It gives you just enough confidence to keep building without proving whether the problem is actually painful.

I think the harder skill is learning to ignore compliments and look for behavior instead.

A polished HTML email can still be clipped by Gmail or render differently in Outlook and dark mode. HtmlDrag Email Compatibility Fixer adds a browser-local preflight step: upload or paste HTML, detect Gmail 102KB clipping risk, missing ALT text, incomplete font fallbacks, dark-mode contrast, Outlook issues, and accessibility gaps.

Users review a selectable list instead of accepting a blind rewrite. They can apply the fixes they want, preview the repaired email, download the HTML, or continue with visual editing for copy and brand adjustments. The compatibility analysis and repair step runs locally in the browser.

The way people talk about the EU AI Act sometimes makes it sound like every startup founder needs to become a lawyer overnight.

From what I've seen, that's probably not the most useful way to think about it.

For early-stage teams, the bigger takeaway is understanding the direction AI regulation is moving. There is increasing attention on risk, accountability, oversight, and the ability to explain how important decisions were made. As AI systems become more involved in customer interactions, business operations, and decision-making processes, regulators seem to be asking for clearer records and more visibility into what happened and why.

What's interesting is that many of these ideas are valuable even outside a regulatory context. If an AI system influences a customer outcome, most teams would probably want to know which model was involved, what information it used, whether anyone reviewed the result, and how the final decision was made. Those questions can come from customers, internal stakeholders, auditors, or simply from the team trying to understand its own systems.

Introducing NeuralAgent's fast computer-use model.
In this example, NeuralAgent's fast model was given a task to go to NeuralAgent's Discord and send an announcement introducing itself as NeuralAgent's fast model controlling Khaled's computer and to write something exciting as well!
It executed the task at lightning speed!
Events in the video occur in real time!

Over the last few months, I've been building salary calculators and researching compensation data across different industries.

One thing surprised me.

People often say they want "more salary transparency," but in practice, raw salary numbers rarely answer the questions people actually have.

Well it's a pretty simple reason or benign reason.

I'm ex-OCI and my former boss is now in charge of DigitalOcean.

The research on how AI systems decide what to trust is clearer than you might think. It comes down to a few core signals that are measurable and actionable.

Citations are the strongest signal. A study from the University of Notre Dame and Deloitte found that simply having citations in an AI response increases user trust significantly even when the citations themselves are random . The presence of sources signals credibility. The act of checking them signals distrust.

E-E-A-T is no longer just Google's framework. It has become the core principle AI systems use to decide what (and who) to trust . AI systems prioritize Experience, Expertise, Authoritativeness, and Trustworthiness when evaluating sources. They look for signs that a real practitioner stands behind the advice detailed bylines, credentials, first-hand narratives, and verifiable experience

Perceived gatekeeping and information completeness matter. Users trust Google because it performs credible gatekeeping . Wikipedia earns trust through collective curation. AI systems look for similar signals: is there evidence of editorial oversight? Is the information comprehensive enough to answer the query completely?

I've noticed that AI governance, AI safety, and AI compliance often get used interchangeably. They're related, but I don't think they're describing the same thing.

The simplest way I've found to think about it is that AI safety is about reducing harmful behavior. If an AI system gives dangerous advice, produces harmful outputs, or behaves in unexpected ways, safety is concerned with preventing those outcomes.

AI compliance is different. It focuses on whether a system meets legal, regulatory, or policy requirements. The question is less "is this safe?" and more "are we meeting the rules we're expected to follow?"

AI governance sits in a slightly different place. It's about ownership, visibility, and control. Who can approve an AI workflow? What records exist? Who is responsible when decisions are made? How can a team understand what happened later if something goes wrong?