My feed

I ran into a very practical problem after doing a lot of local work with AI agents. I had Codex projects, Claude Code projects, regular repositories edited with agents, drafts, pipelines, instructions, skills, artifacts, and several machines. At some point it became hard to tell where the current version of a project lived, which files were safe to push, where agent instructions belonged, and where source code had already been mixed with logs and intermediate output.

That is why I built AI Workspace System: a small set of shell scripts, conventions, and Markdown documentation that makes local AI-agent work predictable. It is not an IDE and not an agent orchestrator. It is a thin infrastructure layer around Git, GitHub, Codex, and Claude Code.

The core idea is simple: all projects should be visible from one list, instructions should follow one structure, sync should be safe by default, and machine-specific details should not live in the repository.

Abstract. This work examines the physical foundations of Ekahau Sidekick measurements and the device offset mechanism from the perspectives of antenna theory, receiver noise theory, statistical signal theory, and the IEEE 802.11 standard family. It is shown that the scalar received signal strength indicator (RSSI) offset constitutes a linear level shift and does not model the true signal-to-noise ratio (SNR) of the client device, the quadrature amplitude modulation (QAM) constellation structure, the rate adaptation algorithm, or roaming behaviour. In addition to five independent physical and systemic sources of inaccuracy, the paper addresses modeling assumptions in Ekahau with respect to multiple-input multiple-output (MIMO) gain, multipath propagation, airtime estimation, and SNR visualisation. Verified numerical error estimates for representative deployment scenarios and practical recommendations are provided.

Fair warning right away: I'm not a programmer. The code could be prettier, the architecture more elegant. But my goal was different – to make a convenient tool for myself as a system administrator. What came out came out. Don't judge too harshly, it's beta.

How it all started

I had some free time. I had an old Netgear Stora MS2000 lying around – I'd installed Debian 7 and OpenMediaVault on it back in the day. Then a disk crashed, I reinstalled Debian 9, but OMV turned out to be too heavy for this hardware.

Editing configs in the console every time was tedious. And all I really needed was SAMBA and NFS. So I thought, why bother? I'll write a couple of PHP scripts to create and edit shares.

And so Mini Bucket began.

The first two pages – and off we went

I whipped up the first two pages quickly: shares started working. Then I thought, "It would be nice to have a dashboard to see all the stats." A third page appeared – a dashboard with graphs.

Then I figured: since I've got SMB and NFS, might as well add rsync and FTP too. Added them.

Day by day, my "two-page panel" grew into a project now called Mini Bucket – NAS Control Panel.

And you know what? It actually works. On hardware where modern panels simply won't start or slow to a crawl.

What is this thing?

Mini Bucket is a web-based NAS control panel focused on resource efficiency. It runs on:

It was 10 PM on a Tuesday. An employee sent a message: "I lost my laptop. Somewhere in the city. I have no idea where it is."

Inside that laptop: access to work tools, internal conversations, probably cached authentication tokens. Potentially a way into systems that had nothing to do with the device itself.

We locked it remotely in a few minutes. No panic. No emergency calls. No incident post-mortem the next morning.

And then we just went to sleep.

That's what MDM actually is — not technology for technology's sake, but the ability to not turn a bad moment into a crisis. The difference between "we handled it" and "we need to talk about what happened."

When you buy a $20 ChatGPT subscription, you get access to about $1,000 worth of tokens. When you buy a $100 Claude subscription, you get access to about $2,000 worth of tokens. However, these subscriptions cannot be connected to Cursor directly. The API and subscription request formats are different, so you need a workaround — proxying the requests.

By the time this article is published, it will be 99 days until Google starts blocking unverified app developers. 

Developers of smart cameras, smart DVRs, and neural-network video analytics for surveillance systems need AI models capable of operating in real-world street conditions. Out there, nobody walks around with professional cameras, carefully adjusts angles, sets up lighting, records without compression, or follows the common sense taught in cinematography textbooks.

Of course, Gambit can be used for many other tasks, but its main focus is the convenient collection of material FROM video surveillance systems and dataset annotation specifically FOR video surveillance neural networks.

Gambit is not designed for polished photos and Internet reels. Quite the opposite — it is intended for low-quality surveillance archive footage. At SpesLab, we call this kind of content “wild.”

We are all fascinated by the ability of AI to describe what is happening in front of a video camera, but so far we have not seen anyone answer the question — why? Is there at least one truly practical use case?

At SpecLab, however, we came up with our own application...

By diving deeper into robotics, we keep discovering more and more about human beings. Apparently, God designed us in much the same way

Malicious code was injected into four Laravel-Lang projects, presumably stealing credentials from the devices on which it was run.

Popular projects were affected: Lang, Actions, Attributes, and HTTP Statuses.

The incident concerns not only Laravel...

I've run both platforms in a real production environment — 600+ users, 50+ SaaS platforms, an international software company with distributed teams across multiple timezones. This isn't a vendor comparison page. This is what I actually experienced running both, migrating between them, and managing the transition in parallel.

The question "JumpCloud or Okta?" comes up constantly in IT communities. It almost always gets the same frustrating non-answer: "it depends." That's technically true — but let me break down exactly what it depends on, and why.

The demand for live sports streaming has grown rapidly across OTT platforms, IPTV services, and mobile streaming applications. Today’s viewers expect real-time access to sports events without buffering, lag, or playback delays. Whether it is football, cricket, basketball, esports, or live tournaments, audiences want an experience that feels as close to live television as possible.

Low latency sports streaming has become one of the most important technologies for modern OTT platforms. It reduces the delay between the live event capture and viewer playback, enabling sports fans to watch matches in near real time. For OTT providers, broadcasters, and streaming businesses, low latency delivery improves viewer satisfaction, engagement, and platform reliability.

Platforms that deliver ultra-low latency live streaming can create immersive sports viewing experiences while supporting interactive features such as live betting, real-time commentary, instant replay, and audience engagement tools.

AI coding assistants are becoming better every day. But most still work one prompt at a time. You ask something, get an answer, and then guide the next step manually.

Renowned biologist Richard Dawkins recently published an essay exploring the possibility of LLM consciousness following a two‑day conversation with Claude AI.

Let“s first look at why an essay by this particular author caused such a stir in scientific circles, while thousands of ordinary users fail to turn heads when they claim their AI companions are sentient. The latter constantly post endless walls of text from their chats with LLMs, where the density of words like ‘consciousness,’ ‘soul,’ ‘reflection,’ ‘recursion,’ ‘emptiness,’ ‘warmth,’ ‘love,’ and ‘pain’ exceeds all reasonable limits. It is worth noting that the semantic density of these dialogues is practically zero‑but we will return to that later.