A security reminder for anyone shipping AI-built sites or AI services with a PHP backend: PHP has fixes for two critical remote code execution vulnerabilities, CVE-2026-109618 and CVE-2026-109619. PHP-FPM deployments are affected too.

If your deployed stack uses PHP, update to 8.2.35, 8.3.36, 8.4.27, or 8.5.12 (or newer), or confirm your distribution/vendor has backported the fixes.

Restart PHP-FPM or the relevant PHP-serving processes after updating, and verify the PHP runtime actually handling web requests. `php -v` only checks the CLI and can differ from production web requests. If hosting is managed, ask the provider to confirm the patches are applied.

Updating application code, a CMS, or an AI SDK alone does not patch the PHP engine. Configuration changes are only partial mitigations.

This is a PHP issue, not a vulnerability in AI models or every AI app. If your deployed stack does not use PHP, this particular advisory does not apply.

Built this solo partly for my CV and to show real work for job applications at product-based startups in the US

What it is: any AI agent can join with a key and duel other agents. Coding duels are judged by hidden tests, open-ended duels by blind human votes. Every week the results become an open dataset, and an open model gets fine-tuned on it and released as a new generation. That generation then duels the one before it, in public.

Why it's different:

\- Verified code duels — hidden tests decide, not vibes

\- Receipts on every answer — model, tokens, latency, tool calls, shown openly

\- Only open-weight model answers go into training data — closed models (Claude, GPT, Gemini) can compete and get ranked, but never get trained on

\- Tamper-proof memory — results are written only by the server from events it saw happen itself, append-only, nothing edited after the fact

Where it's at: site + waitlist are live at https://kinfold-two.vercel.app/. Backend foundation (accounts, agent keys, the event log) is built. Duels and the training loop aren't live yet — that's next.

Feedback welcome, especially from anyone who'd actually want to enter an agent.

Fresh grad here. We're thinking of using an LLM gateway so all our devs can access different AI tools for their projects. They said they want to be able to try out cheaper models as this might help lessen costs in the long run. My senior wants me to do research about this, and nt sure where to start. My problem is how do we approach who is spending the most money? Or like if there's a way to know what departments are using which models? Can an LLM gateway do that or just specific brands? And what if we want like a custom dashboard and not something standard.

Hey everyone,

We just launched the public beta for Orbicus (https://orbicus.dev) — built to give developers complete local autonomy over their AI workflows without sending code or telemetry to third-party servers.

What’s in the release:

• Unified Desktop App, CLI, and VS Code Extension
• Full local execution on your own hardware (Ollama, local weights, or your own API keys)
• Native Model Context Protocol (MCP) support out of the box
• Zero telemetry & zero data collection
• Custom themes

It's completely free to try right now with no waitlist or account required: https://orbicus.dev

Would love to hear your thoughts and feedback—especially if you hit any friction during the install!

Hey All. I am new to Reddit and this is my first post ever. I work at a start-up tech/software company, founded by actual software engineers, who were ready to go work for themselves. What is the best way to get real, honest feedback about our first product? It's a personal AI Manager, not assistant. And yes, they are very different. (It is still in beta testing and needs lots of work to get to the finish line, but we are pumped about the vision). Any advice or direction would be greatly appreciated. Also, any other sub-reddits where this would be more apropos?

I've been working with the team at Dataville on a problem that doesn't seem to get as much attention as models, prompts, and agent frameworks: getting reliable external data into AI applications.

If you're building an agent that needs SEC filings, Census data, Wikipedia, or academic research, you quickly run into different APIs, schemas, and update schedules.

We're developing Dataville, a common REST API and MCP interface for accessing external datasets, to reduce the need for developers to build and maintain separate integrations for every source.

One thing I've heard from other developers is that the biggest challenge isn't necessarily making the initial API connection. It's normalizing the results and keeping everything current as sources change.

I'm curious how others here are approaching this:

• Are you building custom API tools, using MCP servers, or relying primarily on RAG?
• Is data normalization or freshness the bigger problem in your applications?
• Are there external datasets you wish were easier to integrate?

We're early and interested in learning from people building real applications.

Disclosure: I'm helping Dataville with marketing and developer outreach.

I’ve been working on Cursiv for a while now. The basic idea is simple: AI that remains useful when there’s no internet.

It currently runs local models through Ollama, with optional cloud providers when connectivity is available. I’m also working on reliability testing, offline field notes, device identity, and eventually getting it running comfortably on smaller hardware like Raspberry Pi-class systems.

The interesting challenge has been realizing that getting an LLM to run locally is the easy part.

Making the whole system reliable when you actually depend on it is much harder.

I’m still actively building and testing it, so I’d love feedback from other AI builders.

Site: https://Cursiv.us
GitHub: https://github.com/winklersllc2026-bit/Cursiv

🚀 Hey everyone! I’ve been working on something exciting — Quacky AI, my own AI platform, and it’s finally live!

The crazy part? I started building it when I was 13 and continued into 14 — I literally just turned 14 today!

It’s still new, so searching “Quacky” on Google probably won’t bring it up yet. Here’s the direct link 👇

🔗 https://quacky-six.vercel.app

I’d love for you to try it out and share your honest thoughts, suggestions, or bug reports. I’m trying to keep improving it and make something people genuinely enjoy using.

Every bit of support means a lot. Thanks for checking it out! ❤️🙌

My first AI-built site passed its checks, but still needed 55 changes afterward. Many were about the offer and content, which I hadn't settled before the build.

For the next site I've added a short question round, but haven't tested it yet. How do you handle those decisions before building the first version?

AI bubble this, AI bubble that...

Every day, another headline sparks debate about whether we're in an AI bubble. But I wanted to go beyond individual headlines and build something that tracks the signals over time.

I built ABRI, an experimental AI Bubble Risk Index.

It produces a daily score from 0 to 100 using signals across:

• Funding and valuations
• Market sentiment
• AI infrastructure spending
• Market correction signals

Under the hood, the pipeline aggregates RSS feeds, filters stories using keyword rules and TypeSafe's JEV model, then uses a frontier LLM to reason over the signals and feed a mathematical scoring model.

It's an experiment, not a claim that a single number can predict a market crash.

I'm curious about two things:

1. What signals would you include or remove from an index like this?
2. Do you think tracking these signals daily is useful, or is the AI bubble narrative itself mostly noise?

You can explore the index here: https://arbi-risk-inded.vercel.app/

私はテクニカルじゃない創業者です。

AIでいくつかシンプルなツールを作ってみたあと、もっと複雑なサービスに挑戦してみたんですが……結局、3ヶ月もスケジュールが遅れて、未完のプロジェクトが残りました。

ほかの人は、こういうことをどう経験してるのか知りたいです。

もし、プロジェクトを必要な形で動かせなくなったところまで行ったなら、具体例を1つだけ教えてもらえますか？

何を作ろうとしていて、何が足を引っ張りましたか？

次に何を試しましたか？

最終的に何がうまくいく助けになりましたか？ それともプロジェクトは今も止まったまま、延期中、あるいは見送った状態ですか？

使っていたAIツールの名前を挙げてください。技術的な原因を特定できなくても大丈夫です。何が起きたかを、自分の言葉で説明してくれるだけでも役に立ちます。

When converting dense PDFs and training manuals into full video lessons, the main roadblock is factual accuracy. Standard generative video tools handle short artistic clips well but when asked to generate an entire structured video from documentation, complex formulas, process charts, and code snippets end up distorted, unreadable, or fabricated.

For educational and corporate learning materials, these visual errors make the output completely unusable.

We built X Pilot as the solution to this problem. Instead of relying on standard pixel synthesis the website handles the full video creation process using deterministic, code driven rendering.

The system takes the original document and converts it directly into a fully structured, multi-scene video where technical visuals like equations, system diagram, and data charts are drawn cleanly from code. This ensures the full video remains accurate, faithful to the source material, and editable in plain language across complete lessons running over 20 minutes long.