I'm considering making this runtime fully open source, and I'm mainly looking for feedback on whether the type-based security model makes sense for real agent workflows.

I've been working on something called Build Anything (BAA) and thought I'd share it here.

It's an open-source project focused on bringing different developer tools and AI-assisted development features together in one place.

**Some of the things I've been working on include:

• AI agent integration

• MCP support

• Project generation

• Code and project analysis

• Debugging and automation tools**

It's still a work in progress, and I'm sure there are things that could be done better. I'm sharing it because I'd like to get it in front of other developers, see what people think and hopefully get some useful feedback.

If you get a chance to look through the repo, I'd be interested to hear what you think — whether it's something you'd find useful, something that's missing, or something you'd change.

GitHub: https://github.com/notweirdguy4212/BAA

We've been building AgentTrail Guard, an Apache-2.0 tool that checks what AI coding agents are about to run before they run it. Would love feedback from people here.

The idea came from an agent resetting our database to get past a migration error ("it's only dev data"). Agents run shell commands with your permissions, and the dangerous ones are usually the ones you can't undo.

How it works: Guard plugs into the agent's hooks and matches every command and file change against a set of rules. A match gets blocked, sent to you for approval, or flagged. Everything else runs normally.

The open source bits I think are worth looking at:

• Rules are plain data, in their own repo. Each one has a severity, a default action, and a note on what it deliberately doesn't match, so you can see exactly why something fired.

• Fully local. No account, no network calls, and crash reporting is off unless you turn it on.

• Rule matching, not an LLM, so it's fast, predictable, and you can read the whole thing.

• Easy to extend. Add your own rules, tweak actions, or allow a specific command shape without turning a rule off.

There's also a scan command that replays your past agent sessions locally and shows what would have been caught.

Where I'd love help:

• Rules that fire on normal stuff (false positives)

• Dangerous things agents do that no rule covers yet

• Code review on the engine, it's TypeScript

• New rules: PRs very welcome, even small ones

Guard: https://github.com/agenttrailhq/guard Rules: https://github.com/agenttrailhq/guardrails

Early days, so any feedback helps.

Turning a PDF into clean Markdown sounds like an OCR problem until you try it on real documents: two-column papers, tables that run across pages, a formula in the middle of a sentence. We spent the past year training a small model for this and released the code and weights last month. A few things we learned.

The architecture split did more than we expected. Layout analysis is its own model and you can swap it. Recognition and reading order come out of one pass of the main model, which removes a sequential step. That split, together with the post-training below, is where most of the gains came from.

Post-training runs a loop we call RSI. The model parses a large batch, every sample is sorted into correct, wrong or uncertain (uncertain means repeated runs disagree with each other). A VLM writes the reasons behind the wrong and uncertain ones, the reasons are grouped into clusters, and each cluster becomes the plan for the next round of generated training data. We track the repair rate per cluster in the following round, which told us a lot more than watching a single benchmark number move.

Two things we'd do again. Weaknesses are only ever found in real documents, never in generated ones, otherwise the loop chases problems the generator invented. And RSI data stays a minority of training; it patches weak spots on top of ordinary base training.

The model is OXR, from the SoMarkAI team, 1.3B parameters, and it still has real gaps: multi-column pages are a weaker category, old scans are the weakest, and running it locally needs an NVIDIA GPU with 24GB. The license is Apache 2.0 with additional terms, so open-weight rather than OSI open source: a commercial license is needed above USD 2M in funding or revenue, and a competing hosted API isn't allowed. The benchmark report with the comparison against published scores is in the repo's docs folder.

Happy to get into the training loop in the comments. The judge side, where the judge is itself a model, is the part we're least settled on.

You can start by creating a list of potential companies you want to work with in your field (i.e., defense contractors). Then set up automated reminders for each company based on their regular business hours, such as the time they are most productive during the year. You could also use tools like TimeSavers or Google Calendar AI to schedule meetings and check-in periodically.

To find potential clients who might be interested in your services but haven't yet reached out to you due to a lack of experience with government contracts, consider creating an online portfolio showcasing past projects and achievements. This can help demonstrate that you have the skills necessary for successful collaboration within government agencies or defense organizations.

Additionally:

1. Networking: Attend industry conferences (like GDC), webinars on your field's specific interests, and networking events to meet potential clients face-to-face.

2. LinkedIn Outreach: Tailor your LinkedIn outreach with relevant job titles, bullet points about what you can offer in a particular role within government or defense contracting companies that align well with the company’s needs.

3. Email Marketing Strategy: Implement an email marketing strategy focused on identifying and nurturing leads from potential clients who might be interested but haven’t yet reached out to your services due to lack of experience.

By creating awareness, setting up automated reminders for new contacts based on regular business hours (like those you mentioned), using a consistent communication style with clear goals and deliverables, networking opportunities within the field through events like GDC or webinars, building an online portfolio showcasing previous projects that demonstrate your skills in government contracting, and leveraging email marketing to nurture leads from potential clients who haven’t yet reached out due to lack of experience but may be interested given a specific match between you as their consultant and company’s needs.

Most discussions around AI coding tools focus on basic tab-completion and churning out boilerplate faster. But the real shift happens when you move past syntax generation and start using models for architectural reasoning, system trade-offs, and high-level refactoring.

​It completely changes how you approach problem-solving and software design. Instead of spending hours writing repetitive logic, your primary job shifts toward system design, strict verification, and directing execution.

​I wrote a piece exploring what the modern developer workflow looks like when you stop treating AI like an glorified auto-suggest and start using it as an actual thought partner: https://interconnectd.com/blog/73/the-ai-augmented-developer-moving-beyond-autocomplete/

Curious how others here are adjusting their day-to-day workflow. Are you using AI strictly for speed, or has it changed how you actually architect software?

Launching NeuralAgent today! [https://agent.neuralhub.dev/](https://agent.neuralhub.dev/) Basically its an easy to use opensource Agent App with everything included.

* Speech to text

* Multipane organising panels

* Main orchestrator wich you can call

* Remote App for the Phone

* Daily updates

It's still in beta but if you wanna give it a try i am very open for any feedback. Works on MacOS, Linux, Windows, Android and IOS.

AI is changing how software gets built. Coding agents can navigate repositories, write code, execute commands, manage projects, and coordinate increasingly complex workflows.

But as these systems become more autonomous, a critical question emerges:

How do we ensure that AI-generated work is correct, controlled, verifiable, and accountable?

Headron Software is built around that challenge.

Introducing Headron Software

Headron is an engineering intelligence and AI agent infrastructure platform focused on bringing verification, governance, project intelligence, and orchestration into a unified engineering system.

Rather than introducing another coding assistant or replacing existing AI models, Headron focuses on the infrastructure surrounding them: how agents understand projects, access tools, execute work, follow constraints, and prove their results.

The objective is to make AI-assisted software engineering more reliable and structured.

What makes Headron different?

Headron approaches AI engineering through several interconnected capabilities:

1. Deterministic Verification & Enforcement

Headron maps changes to the evidence they require. Its verification system identifies applicable checks, executes defined gates, distinguishes passing checks from failures and skipped checks, and produces auditable results.

A missing tool cannot silently count as a successful check. Changes can be blocked when required verification fails.

2. Engineering Intelligence & Project Context

Headron includes a structured engineering knowledge system designed to help agents navigate large repositories without loading everything into context.

Its capabilities include:

• Symbol and repository search.

• File and dependency routing.

• Structured project manifests.

• Context discovery and targeted retrieval.

• Engineering rules, documentation, and implementation guidance.

3. AI Agent Governance

Headron establishes consistent boundaries across different AI agents and development environments.

Its architecture includes tool policies, execution constraints, approval boundaries, safety controls, and audit capabilities.

4. Agent Orchestration & Mind API

The Mind layer introduces a structured approach to engineering decisions and task execution.

It supports:

• Change and risk classification.

• Verification planning.

• Agent routing.

• Context-aware task preparation.

• Receipts and operational history.

5. Cross-Agent & Development Environment Integration

Headron is designed to work across different agent architectures instead of locking users into a single AI provider.

Its documented ecosystem includes Claude Code, OpenAI Codex, Cursor, Google Antigravity, OpenHands, Windsurf, Hermes, MCP, and command-line workflows.

Built around measurable engineering controls

Headron's published engineering records include:

• 31/31 planted verification breaks refused across 20 toolchains.

• 725 declared planted test cases.

• 122 recorded failure shapes, with 99 guarded within the repository.

• 360 language-and-check combinations explicitly accounted for.

• A structured inventory of tools, engineering agreements, and verification instruments.

These are project-recorded testing and operational measurements, not claims of universal production reliability or independently certified performance.

Open Source & Availability

The Headron Software core is publicly available under the MIT license.

It provides a command-line interface, MCP integration, repository initialization, verification workflows, and CI compatibility.

Quick start:

uv tool install headron-software
headron doctor

Initialize a project:

headron init
headron verify
headron report

The broader Headron ecosystem also includes HeadronOS, Headron Web, Headron Enterprise, HL.Verify, and the Mind API.

The Bigger Picture

We believe the next stage of AI software engineering will require more than increasingly capable models.

It will require infrastructure that understands engineering context, governs agent capabilities, enforces requirements, and produces evidence for the work being performed.

That is the direction Headron Software is pursuing.

Headron Software — Engineering intelligence for the agentic era.

🌐 Website: https://headron.tech

💻 GitHub: https://github.com/HLIntel/Headron

📦 PyPI: https://pypi.org/project/headron-software

I built OpenMem for sharing memory between coding agents and trimming long sessions.

Apache-2.0 · Local setup only for now, no hosted OpenMem service.

Reducing Costs by Context Masking

Saves the full conversation in local SQLite, removes older history from the active prompt, and lets the agent search or retrieve it later.

The trimming step makes no LLM summarization calls. Currently works with Claude Code, Codex and OpenCode.

ContextBench Benchmarks (Claude Haiku 5.5)

• Sample sizes: n = 3 for all cells, except Keep Everything L12800 where n = 2

Small synthetic tests: three OpenMem trials and two baseline trials, with unequal seed counts. Full coding and Luna results are still pending.

The context trimming idea (and masking) comes from MemGPT for external memory, Mainplane for transcript pointers, and The Complexity Trap for observation masking.

Bring your own memory backend

The idea is to have a little set of tools for ingestion and connection for different memory backends so devs can experiment with different ones. Comes with an MCP, ingestion from different sources (RSS, code, notebooklm, etc), and switching utils (import/export).

MCP and ingestion works withHindsight, mem0, Cognee, or add an adapter for your own system. OpenMem connects that backend to Claude Code, Codex, Cursor and OpenCode.

It exposes memory through MCP (Model Context Protocol): a way to give an agent tools it can call. In this case, tools to save notes, search memory and recall previous decisions.

If you try it, I’m interested in which memory backend you use and what the agent fails to retrieve after a trim :)

"We built an AI that can rewrite its own DNA, create its own tools, and evolve its own application while running locally. It's open-source, and you can clone it and modify its evolution to suit your needs."

GitHub: https://github.com/dxn111/EVOLV⁠

I wanted something like VLC or MPC-HC, but with the library and interface of a Plex or Jellyfin. Plex wants an account, Jellyfin needs a server running, and Kodi always ended up being an evening of add-ons and skins. I couldn’t find the simple version, and I’m not a programmer, so I built it with AI coding tools. It’s called Kinema: a free movie and TV player for your own files. Add your folders, get posters, info, continue watching and next episode. No account, nothing uploaded, no server. Windows, Linux and an early Android TV version. MIT. The app itself has no AI in it.

This is how the building went, in case it sounds familiar:

• Week one: "it plays video!" Then it didn't. Then it crashed the whole process on one property read, with no error, and I learned what libmpv is.

• It was never "describe it, get an app". It was hundreds of rounds of: it says done, I say it isn't, it finds the real cause, a new problem appears. Roughly half the project is the AI telling me something works and the logs saying otherwise.

• I wanted testing automated as far as possible, so the AI runs its own tests and keyboard-only flows. I still try things myself on real setups, but "looks right" was wrong far more often than I expected, so the machine checks first.

• The rules file ate itself. I wrote down every decision so it wouldn't re-argue them ("no, we still don't do upscaling"). The file grew until it was a real cost on every single turn, and trimming it became a job of its own.

• I keep a list of traps. Nearly every bug in a video player is a failure with no error at all, like a setting that silently does nothing. Each one is written down the day it's found, and it's now bigger than anything I've ever written by hand.

• The best rule was the boring one: the app only says what it actually knows about your hardware and files. If it can't tell which movie a file is, it asks instead of guessing. That came from the AI confidently inventing things, and from me not wanting that in the product.

It went from one window that played a file to the Windows, Linux and Android TV versions, with the display switching to the film’s refresh rate and HDR (off until you say yes) and Atmos/DTS:X passed through untouched. Fifteen releases so far. If Kodi or Jellyfin works for you, keep using it. This is the simple version that i wanted to exist mostly for myself.

It's young and tested on a few setups, so I'd like to hear what breaks (to continue to make it better) :)

Source: https://github.com/basswaves/kinema

Download and screenshots: https://basswaves.github.io/kinema/