Let me know your thoughts: https://rssamplifier.com/

For details, visit: www.pipelang.com.

Over the past few weeks I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).

The idea is simple:

1. Capture — dump whatever's on your mind, instantly, no structure required. This step does nothing clever on purpose — it just writes your text to an immutable inbox. Zero chance of losing an idea to a bug or a slow API call.
2. Organize — on demand, an LLM reads your unprocessed notes and extracts people, projects, tasks, decisions, meetings — then deterministic Python (not the LLM) creates/merges the actual wiki pages and maintains backlinks. The model only reasons; it never touches the filesystem directly.
3. Ask — query or chat with your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.

Why I built it this way:

• Plain Markdown + YAML frontmatter, no database. Every page is a .md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.
• No vector DB by default. At personal scale (hundreds–low thousands of pages), keyword search + a real link graph (related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.
• Immutable inbox. Your raw notes are never edited or deleted by the organizer. If the LLM mis-extracts something, your original words are always still there.
• Any LLM. Built on LiteLLM, so point it at OpenAI, Anthropic, Gemini, Mistral, or a fully local model via Ollama — one config value.

It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.

There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.

Repo: https://github.com/Aaryanverma/graybox

pypi: pip install graybox

I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.

It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.

Token usage is the lines-of-code metric of the AI era. The industry knows it. It just hasn't agreed on what comes next....

Meta built a leaderboard ranking engineers by how many AI tokens they consumed. It has since been taken down, but the impulse behind it hasn't gone away.

Across engineering organizations, there is enormous pressure to prove that the millions being spent on AI tooling are paying off. When that pressure mounts, leaders reach for the easiest number available.

Token usage is objective, automated, and scalable. It's also easy to game and almost entirely disconnected from whether AI is actually making engineers more productive.

As one engineering leader put it: "I wouldn't be surprised if we see the opposite trend next year, aiming for efficient usage of tokens as opposed to celebrating burning them at expensive rates."

So what should organizations be measuring instead?

Full article available here: https://leaddev.com/ai/tokenmaxxing-and-the-search-for-ai-metrics-that-matter

Is it bad that it took me over year to build this web app?

I'm sure experienced developers could have built something like this faster, but I learned a lot while making it and I also spent a lot of time iterating it based off of feedback, so I'm genuinely proud of how far it has come.

It's a checklist app for repeatable routines that only resets when you finish every task. Instead of daily streaks it's based on completion. One unique thing about this is that it resets automatically when you finish so you can complete it again immediately. I also added light gamification to help with the repetition of completing the same tasks over and over again.

Sometimes when you're doing the same routine repetitively, it's easy to for things to get overlooked. I originally built this because, as an aerospace inspector, missing a step is not an option.

It may not be the flashiest app, but I spent a lot of time making sure it can handle real routines, work offline, require no account, and keep data local on your device.

https://minicycleapp.com

GitHub:
https://github.com/sparkincreations/minicycle

I'd appreciate any honest feedback on the website, app idea, or first impression.

https://leaddev.com/ai/the-throughput-trap-ai-powered-teams-ship-more-code-but-deliver-less

AI-coding tools and harnesses have changed the role of software engineering – and fast. Devs have started tokenmaxxing, with some companies, including Meta, launching leaderboards to measure their workers’ ability to burn through AI usage.

Agentic coding tools have radically changed AI usage, acting semi-independently through command lines, files, internet access, and tool calls. This also means they burn far more tokens than older chat-based tools, says Sebastian Baltes, professor of software engineering at the University of Heidelberg. “I’m a daily user of these tools myself, and it’s just astonishing how fast the field moves,” he says.

As software engineering evolves, the suppliers of that AI inference, including the biggest providers within the tech sector, have pulled the rug from beneath coders and the companies they work for.

Price hikes, tighter usage limits, and the shutting down of some providers to new signups has upended the new way of work software engineers were just getting used to.

Read the full article: https://leaddev.com/ai/your-ai-coding-budget-just-got-a-lot-more-complicated

Today, we are officially announcing Early Access for our latest and most advanced model, Horus Cyper Nano 1.0 BETA.

We are making Horus Cyper Nano 1.0 BETA available to developers, researchers, and students through our Early Access program.

You can apply through the official Early Access portal. Once you meet the required eligibility criteria and your application is approved, you will receive your personal Access Token, which can be used through our NeuralNode Framework to access and integrate the model.

Apply for Early Access:
https://tokenai.llc/horus-cyper-nano-access

Horus Cyper Nano is a specialized cybersecurity model designed for offensive security and cybersecurity research workflows.

Its core use cases include:

Offensive security and red teaming, including penetration testing workflow support, vulnerability analysis, and exploitation path building.

Capture The Flag challenges and cybersecurity training.

Active Directory security, including enumeration and lateral movement planning within authorized engagements.

Authorized security testing labs and controlled environments.

Safe and scoped cybersecurity research within authorized environments.

Red team report drafting and attack chain structure planning.

Horus Cyper Nano 1.0 will be the first release in the Horus Cyper series, a family of specialized cybersecurity models developed by TokenAI, an AI startup based in Egypt.

The Open Weights of Horus Cyper Nano 1.0 will be released on September 3, 2026, which also happens to be my 19th birthday.

What a way to celebrate.

Our vision is to build Horus Cyper Nano into one of the strongest cybersecurity AI models to emerge from Egypt, the Arab world, the Middle East, and Africa, and to establish it as one of the leading openly available cybersecurity models across the region.

This is only the beginning of the Horus Cyper series.

Horus Cyper Nano 1.0 BETA
Developed by TokenAI
Built in Egypt