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RuView is an open-source web-based application that provides a user-friendly interface for managing and interacting with Docker containers. It offers features such as container management, image management, and real-time monitoring of container performance metrics.

I'm building a tiny embeddable VM in Rust, where the core primitive is a Task with its own state and executable bytecode instructions.

Recently I added new example: Implementation of Binary Coded Decimal decoder from Petzold's "Code" book — every logic gate (bits switch, inverters, AND gates) is a task carrying its own instructions.

It works simple : cargo run 1001 -> 9 and as output it will show AND gate with inputs and output

bcd-decoder: https://github.com/tracyspacy/spacydo/tree/main/examples/bcd-decoder

The article describes the development of a dependency-free GPT (Generative Pre-trained Transformer) model running on a custom operating system. The author discusses the challenges of building a self-contained AI system without relying on external dependencies and libraries.

The article argues that progress towards Artificial General Intelligence (AGI) is moving faster than many experts believe, and that the risks associated with AGI are being downplayed or overlooked by the public and policymakers. It suggests that the development of AGI could have significant societal and economic implications that need to be carefully considered.

Windhawk is a powerful open-source security tool that allows security professionals and system administrators to monitor and analyze network traffic on Windows systems. It provides comprehensive network visibility, enabling users to detect and investigate suspicious activities, malware infections, and other security-related issues.

Oxmgr is an open-source process manager for Linux, macOS, and Windows. Language-agnostic (Node.js, Python, Go, any executable), declarative oxfile.toml config with idempotent `oxmgr apply`.

Benchmarked against PM2 on Linux (GitHub Actions Ubuntu, automated on every push): - 42x faster crash recovery (3.7ms vs 157ms) - 21x lower memory at 100 processes (6.8MB vs 145MB) - 7.5x faster fleet start (773ms vs 5.82s) - 47x faster single process start (3.9ms vs 184ms)

Benchmarks are reproducible — script is in the repo.

GitHub: https://github.com/Vladimir-Urik/OxMgr Benchmarks: https://oxmgr.empellio.com/benchmark

The article discusses the trend of corporatization, where corporations are gaining increasing influence and control over various aspects of society, including politics, media, and public services. It examines the potential drawbacks and implications of this phenomenon.

The article discusses the benefits of using fast servers, such as improved performance, reduced latency, and increased reliability. It provides guidance on evaluating and selecting appropriate server hardware and configurations to meet the needs of various applications and workloads.

I feel like the hustle culture is more prominent and celebrated these days. But I'm curious to know if there are still companies out there that prefer to keep meetings to a minimum, or none at all, to optimize for autonomy, trust, and giving people the space to do their work in silence.

A few companies that come to my mind now that still work this way are: doist, dnsimple, Cliniko, Calibre, HeadshotPro.

Any others?

The article explores Gemini CLI, Google's free coding assistant tool that helps developers write code more efficiently. It provides an in-depth explanation of Gemini's features, capabilities, and how it can be leveraged to streamline the coding process.

Gnosis is a decentralized prediction market platform that allows users to create and trade on the outcome of future events. It aims to provide a trustless, transparent, and censorship-resistant way for people to speculate on the likelihood of real-world outcomes.

A total lunar eclipse, also known as a 'blood moon', will be visible across much of North America and South America on March 14, 2025. Skywatchers are encouraged to capture the event with their smartphone cameras, as the eclipse will create a dramatic red-orange hue in the night sky.

The article discusses the importance of implementing well-designed agent policies to guide the behavior of AI systems. It highlights the need for clear guidelines and ethical principles to ensure AI agents act in alignment with human values and interests.

I built an anonymous messaging app that runs as a Telegram Mini App. Share your link, friends send anonymous messages, unlock them with Telegram Stars (no credit card needed).

Tech stack: Node.js/Express, PostgreSQL, React, Telegraf. Single Hetzner VPS with PM2. Total cost: ~$5/month.

Interesting technical bits: - Auth uses Telegram's initData with HMAC validation (no passwords) - Payments via Telegram Stars API (createInvoiceLink → pre_checkout → successful_payment) - Haptic feedback makes WebView feel native - i18n for English + Russian

Try it: https://t.me/WhisprMe_bot

Happy to answer questions about the Telegram Mini App platform or Stars payment flow.

Spec + Data

Not a fully formed thought, and apologies for the brain fart, but...

This post was inspired by an article on latent.space about how, first, human coding "died". Then now we're going to see the demise of the "pull request".

If you're not familiar, a pull request, or "PR", is a request to change some code in software, by introducing some new code that you have written yourself. So someone has to review that code, make sure it's all written "properly" and then approve or reject it.

Code can now be generated 1000x faster by AI. And AI generated code still needs to be merged into the main code. And if every chunk of AI generated code needs a pull request, that's thousands of pull requests to review by humans. We then become the bottleneck.

Then why not get AI to do the review? It can do it much faster than humans. The @latentspacepod article asks why have pull-requests at all? It was AI generating code. Why have the same AI look at it again?

Personally, I disagree, for now anyway. I think we're in a transition period where humans still need some control. And while we don't write code anymore, and I do believe PRs should be reviewed by AI, I think humans should still manage the PR agents - for example defining rules for the review agents to follow, checking that they did follow the rules, etc.

But that's transitional, and besides my point. What I do agree with in article is where humans will be needed. And where we're needed is further up the software development chain, into the realm of requirements and testing.

Code is not created in a vacuum. It is created to automate work. It is created to fulfill a specific requirement. And this requirement will still be created by humans.

And if we can be clear about what we need - the requirements - then we can also run automated tests to check that the code meets our requirements.

If code is cheap, and instant, and humans stop become the bottleneck to deploying code, what does this mean for code ownership?

It means it doesn't make sense to "own" code anymore. So what's worth owning?

Spec and Data.

How your organisation runs, all its rules, all its goals. These make up your software specification (spec).

All the information your org collects, all the information it needs to run. That's your data.

If you have a clear, tight, spec. And a database that holds all your data. Then you can reproduce all the software that you need instantly.

An AI can read your spec. Then create automated tests against that spec. Generate all the code needed. Connect to your data. Test the code against the spec. Verify that it is all correct. Deploy your code. And hand you the keys (login) so you can drive it straight away.

This has several further implications:

- Should an org's investment now turn to speccing and testing?

- Should we work on a more standard way of defining spec and data schemas?

- What does this mean for software development agencies, if producing and reviewing code is not needed anymore?

- When an enterprise buys from Google and Microsoft, what are they really buying, if it's not software anymore?

Definitely more questions than answers. And things will continue to change fast for the software development industry.

And for organisations - if you already create and document great spec, and have automated tests in place, and have good data policies, then you're in a great place.

China has unveiled the world's fastest bullet train, the CR450, with a top speed of 350 km/h. The new high-speed train represents a significant advancement in China's rail technology and infrastructure.

I'm working on a vendor review workflow where the output is not "an AI answer" but artifacts you can actually use:

- Evidence Pack (PDF) with citations for every claim - Diff report (what changed between contract versions) - Approval log (who approved what, based on which evidence)

If a claim can't be backed by evidence, it's marked Unknown.

Demo (synthetic data, no customer data): https://b2apps-hwbghfetgtamcmfb.japanwest-01.azurewebsites.n... Sample deliverables (PDF/MD): https://b2apps-hwbghfetgtamcmfb.japanwest-01.azurewebsites.n...

Questions: - In your process, where does most time go: collecting evidence, reviewing diffs, or getting approvals? - Would "questionnaire-only" (no sensitive docs) be useful as a first step?

The article discusses the concept of fixpoints in programming, which are values that remain unchanged when a function is applied to them repeatedly. It explains how fixpoints can provide clarity and simplicity in code, and outlines the steps to identify and utilize fixpoints to improve the design and understanding of software systems.

The article describes Nino Ivanov's project to bring the classic DEC PDP-8 minicomputer from the 1960s to life using an Arduino board. The project aims to recreate the PDP-8's functionality and user experience on a modern platform, allowing people to interact with this historic machine in a new way.

EU legislation (which affects UK and US companies in many cases) requires being able to truly reconstruct agentic events.

I've worked in a number of regulated industries off & on for years, and recently hit this gap.

We already had strong observability, but if someone asked me to prove exactly what happened for a specific AI decision X months ago (and demonstrate that the log trail had not been altered), I could not.

The EU AI Act has already entered force, and its Article 12 kicks-in in August this year, requiring automatic event recording and six-month retention for high-risk systems, which many legal commentators have suggested reads more like an append-only ledger requirement than standard application logging.

With this in mind, we built a small free, open-source TypeScript library for Node apps using the Vercel AI SDK that captures inference as an append-only log.

It wraps the model in middleware, automatically logs every inference call to structured JSONL in your own S3 bucket, chains entries with SHA-256 hashes for tamper detection, enforces a 180-day retention floor, and provides a CLI to reconstruct a decision and verify integrity. There is also a coverage command that flags likely gaps (in practice omissions are a bigger risk than edits).

The library is deliberately simple: TS, targeting Vercel AI SDK middleware, S3 or local fs, linear hash chaining. It also works with Mastra (agentic framework), and I am happy to expand its integrations via PRs.

Blog post with link to repo: https://systima.ai/blog/open-source-article-12-audit-logging

I'd value feedback, thoughts, and any critique.

The article discusses the formation and activities of a team of intelligence agents, known as the Squad of Agents, who work to uncover conspiracies and protect national security. It highlights their unique skillsets, investigative techniques, and the challenges they face in their high-stakes missions.