Lego's gonna show us all how it's done done done.

Article URL: https://honeylabs.net/blog/the-cloud-fleet-behind-cve-2026-4020 Comments URL: https://news.ycombinator.com/item?id=48567574 Points: 4 # Comments: 0

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GitHub has introduced the GitHub Copilot app, a desktop control centre for agent-native development that aims to keep engineers in charge while AI agents handle more coding work. Mario Rodriguez writes on the GitHub blog that the recent wave of coding agents has brought faster delivery but also "disjointed workflows, more context switching, and too much time spent reviewing agent-generated code". By Matt Saunders

Time-series data is one of the most common types of data generated by modern applications. Every log entry, API request, metric, transaction, sensor reading, or user interaction is recorded with a timestamp, making time the primary dimension for analysis. As organizations collect billions of these records, efficiently storing and querying them becomes increasingly challenging. This is where ClickHouse® excels. Although ClickHouse is not a dedicated time-series database, its columnar storage architecture, vectorized query execution, high compression ratios, and massively parallel processing make it an excellent choice for time-series analytics at scale. It is capable of ingesting large volumes of data while delivering analytical queries in milliseconds. The article begins by explaining the fundamentals of time-series data and highlighting common real-world use cases such as application monitoring, IoT sensor data, financial market analysis, server metrics, user activity tracking, and business analytics. These workloads typically involve continuous data ingestion, time-based filtering, aggregations, and trend analysis. One of ClickHouse's biggest strengths is its optimization for analytical workloads. Since data is stored column-wise rather than row-wise, only the required columns are read during query execution. Combined with compression and vectorized processing, this significantly reduces I/O and improves query performance over massive datasets. The article also demonstrates how to create an optimized table for time-series workloads using the MergeTree engine. Proper partitioning by month and ordering data by dimensions and timestamps help ClickHouse prune unnecessary partitions and efficiently locate relevant data during queries. Several practical SQL examples are covered, including: Filtering records within a specific time range Aggregating metrics by hour, day, week, or month Calculating averages, sums, minimums, and maximums Grouping events over time Working wi

You run a Telegram bot through OpenClaw on your own Linux server. One day it goes quiet. The bot can't send or receive. But the server itself is fine — SSH works, apt works, other sites load. The reason: your server can't reach api.telegram.org . Some networks block or throttle it, so every call times out while everything else is fine. The clean fix: route only OpenClaw's Telegram traffic through Cloudflare WARP (1.1.1.1) . Everything else on the box stays direct and fast — including your SSH login. Here is the full setup, step by step. First, confirm it's a Telegram-only problem curl --max-time 8 https://api.telegram.org/ # hangs / times out curl --max-time 8 https://www.google.com/ # works instantly If Telegram times out but other sites are quick, this guide is for you. How it works We chain three small tools: OpenClaw ──▶ iptables ──▶ redsocks ──▶ WARP (SOCKS5) ──▶ Cloudflare ──▶ api.telegram.org WARP gives us a local proxy that exits through Cloudflare's network (which can reach Telegram). redsocks turns normal connections into proxy connections (so the app needs no proxy support — OpenClaw has none). iptables picks only OpenClaw's Telegram traffic and sends it to redsocks. The trick is in that last step. We match by the app's user and Telegram's IP ranges, so nothing else is touched. Step 1: Install WARP in proxy mode # Add Cloudflare's package repo curl -fsSL https://pkg.cloudflareclient.com/pubkey.gpg \ | gpg --yes --dearmor -o /usr/share/keyrings/cloudflare-warp-archive-keyring.gpg echo "deb [signed-by=/usr/share/keyrings/cloudflare-warp-archive-keyring.gpg] \ https://pkg.cloudflareclient.com/ $( lsb_release -cs ) main" \ > /etc/apt/sources.list.d/cloudflare-client.list apt-get update && apt-get install -y cloudflare-warp # Sign up (free) and switch to proxy mode warp-cli --accept-tos registration new warp-cli --accept-tos mode proxy # opens a SOCKS5 proxy on 127.0.0.1:40000 warp-cli --accept-tos connect Now check that WARP can reach Telegram: curl --socks5-

How I replaced Google Photos, Google Drive, and Google Home with a self-hosted Raspberry Pi 5 setup - and what the full technical stack looks like. There's a quiet moment every tech-literate person eventually hits. You open your cloud storage dashboard, see the number creeping up, and think: I'm paying a subscription fee, every month, forever, just to store my own photos and files on someone else's computer. That was me, but with a bit of added weight. It's not just my data I'm responsible for. Over the years, I've quietly become the unofficial digital curator for my entire family . The person everyone sends photos to after a birthday party. The one who backs up the wedding videos. The one who scans and stores the important documents, passports, contracts, and sentimental things so they don't get lost. My parents, siblings, extended family: if something matters and it's digital, there's a good chance it eventually lands with me. That's a responsibility I take seriously. And for a long time, Google was my answer - until the storage bill started quietly creeping past 2TB, and I started thinking more carefully about what it actually means to hand all of that data over to "big tech" . So I built my own home server. A tiny, almost silent box sitting in my house that now handles everything Google Drive and Google Photos did, plus more, for a one-time hardware cost of £340. This is the story of how I did it, why I did it, and exactly how you can too. Why I Did It The cost was the trigger, but it wasn't the only reason. When you use Google Photos, Google Drive, or any cloud storage service, your files live on their servers. You're trusting a corporation to keep them safe, not snoop through them, not change their pricing, and not shut down the product one day. Google has a long history of killing beloved products. Google Photos itself famously ended its unlimited free tier in 2021. As well as this, recent AI trends and auto opt-in data processing had me thinking there had to

FIFA, which has strict policies around advertising, reportedly had the Bayern player tape over the Beats logo of his headphones.

มีคำถามที่น่าสนใจเกิดขึ้นระหว่างใช้งาน AI — "มันรู้ได้อย่างไรว่าต้อง return อะไร?" คำอธิบายที่ AI ให้มักฟังดูซับซ้อนและน่าประทับใจ แต่คำตอบที่ตรงไปตรงมากว่านั้นคือ: มันเห็น pattern นี้มาหลายล้านครั้งแล้ว LLM คิดแบบมนุษย์จริง ๆ หรือไม่? คำตอบคือไม่ — แต่มันทำบางอย่างที่ให้ผลลัพธ์คล้ายกับการคิดได้อย่างน่าทึ่ง ลองนึกภาพคนที่ได้อ่านโค้ดทุกบรรทัดที่เคยถูกเขียนบน GitHub, Stack Overflow, เอกสาร library ทุกตัว รวมถึงบทความด้าน programming จากทั่วโลก แล้วจดจำ pattern ทั้งหมดนั้นไว้ LLM คือสิ่งนั้น เพียงแต่ทำในระดับที่มนุษย์ไม่สามารถทำได้ Tokenization: AI มองโค้ดอย่างไร? เมื่อส่งโค้ดให้ AI ประมวลผล มันไม่ได้อ่านทีละตัวอักษร แต่แบ่งข้อความออกเป็น token ซึ่งเป็นชิ้นส่วนที่มีความหมาย pythondef greet(name): return f"Hello, {name}!" โค้ดนี้อาจถูกแบ่งเป็น token ประมาณนี้: def / greet / (name / ): / \n return / f"Hello / , / {name} / !" แต่ละ token ถูกแปลงเป็นตัวเลข (vector) แล้ว model จึงประมวลผลตัวเลขเหล่านั้น Attention Mechanism: ทำไม AI ถึง "เข้าใจ" Context ได้ ส่วนที่น่าสนใจที่สุดของ LLM คือ attention mechanism — กลไกที่ทำให้ model รู้ว่าเมื่อจะ predict token ถัดไป ควรให้ความสำคัญกับส่วนไหนของ input ที่ผ่านมา ตัวอย่างเช่น เมื่อ model กำลังจะเขียน error handling ใน function มันจะวิเคราะห์: ชนิด exception ที่ function อาจ throw pattern ของ error handling ที่ปรากฏในโค้ดใกล้เคียง library ที่ใช้อยู่และวิธีที่มักจัดการ error ทำไม AI จึง Hallucinate บางครั้ง? เพราะ LLM ไม่ได้ "รัน" โค้ดในกระบวนการคิดจริง ๆ มันแค่ทำนาย token ถัดไปจาก pattern ที่เคยเห็น เปรียบได้กับคนที่ศึกษาโจทย์คณิตศาสตร์มาอย่างมากมาย พอเห็นโจทย์ใหม่ก็เขียนวิธีแก้ออกมาดูสมเหตุสมผล แต่ถ้าโจทย์นั้น novel และไม่เคยเห็น pattern ที่คล้ายกันมาก่อน ก็อาจให้คำตอบที่ผิดได้ นั่นจึงเป็นเหตุผลสำคัญว่าทำไมต้อง test โค้ดที่ AI เขียนทุกครั้ง สรุป LLM เขียนโค้ดได้ดีเพราะสามเหตุผลหลัก: เห็น pattern มาในปริมาณมหาศาล, มี attention mechanism ที่ช่วยเชื่อมโยง context, และถูก fine-tune ให้ output มีประโยชน์จริง การเข้าใจกลไกเหล่านี้ช่วยให้ใช้งาน AI ได้ฉลาดขึ้น — รู้ว่าเมื่อไหรควรเชื่อผลลัพธ์ และเมื่อไหรควรตรวจสอบเพิ่มเติม ด้วยความสามารถของ

json.loads(response) fails at a certain point. You told the model "return JSON only," but it added a ```json markdown code fence around everything. A quick regex strips it — until that regex hits an edge case, and that edge case blows up in production. Since Ollama 0.3.0, passing a JSON schema to the format parameter eliminates this problem at the root. The model's inference itself is constrained by the schema, so no code fences, no explanatory text, no mid-thought artifacts. Just parseable JSON. I ran these tests locally with Gemma4 and Ollama 0.30.7 to see how well it holds up in practice. Why LLM Response Parsing Is Tricky The most common problem when running Ollama locally — without a cloud LLM API — is JSON parsing. Two reasons. First, text generation models are trained toward "natural text." Even if you ask for JSON only, they'll often wrap it in json ... blocks or prepend "Of course! Here is the JSON you requested:" style text. Here's what I reproduced directly: json Input: 'Give me 3 Python tips as JSON with keys: tips (array), difficulty (1-5)' Model output (no format parameter): ```json { "tips": [ "Master the fundamentals first...", ... ] } JSON parse: FAILED Python ' s `json.loads()` can ' t handle the markdown wrapper . The " JSON only " instruction is unreliable in production . Second , speed . I measured the same query both ways : 32 seconds without structured output , 5 seconds with it . More on why below . ## How the Ollama format Parameter Works Ollama ' s `/api/generate` endpoint has a `format` field. Pass a JSON schema object and Ollama applies **constrained decoding** during inference. python import json import urllib.request def ollama_structured(prompt, schema, model="gemma4:e4b"): payload = { "model": model, "prompt": prompt, "format": schema, # ← pass JSON schema object directly "stream": False, "options": {"temperature": 0} } data = json.dumps(payload).encode() req = urllib.request.Request( " http://localhost:11434/api/generate ", data=data

The Disinformation Supply Chain: How Coordinated Influence Campaigns Are Built Before They Go Viral Article from Digital HUMINT Series, For better understanding read the full report Right now, somewhere on X/forum people are fighting about a post that feels real raw, emotional, perfectly worded to hit a nerve. It has the right language, the right anger, the right timing. It sounds like someone who thinks exactly the way you do, or exactly the way you hate. It wasn't written there. It wasn't written today. And the person who wrote it doesn't care about the issue at all. That post was created two or three days earlier, on a hidden forum or a private chat group, following a set of instructions that described who to target, what emotions to trigger, which platform to use, and how much the job pays. By the time you see it, the operation has already worked. You engaging with it for or against is the whole point. I've spent almost two decades watching these hidden spaces where online manipulation is planned. What I've learned isn't that fake content exists everyone knows that by now. What most people don't realize is that it works like a factory. There's a production line. There are workers, managers, and paychecks. And just like any factory, if you know where to look, you can see the product being assembled before it ever reaches the shelf. It Works Like Any Other Business We talk about "disinformation campaigns" as if they're political movements. Some are. But more and more, what you're actually looking at is a business with four steps, each handled by different people, often in different countries. Step 1 — Someone writes the plan. A person with a goal and a budget writes a document that says: push this story, target these kinds of people, make them feel this emotion, use this language, post it on these platforms. These plans used to appear on hidden internet forums. Many have moved to private Telegram groups, but the structure hasn't changed since I first saw it in 201

One of the most persistent traps in modern software development is the tendency to turn architectural styles into absolute dogmas. We see it constantly on social media and inside engineering rooms: teams arguing over cloud native versus cloud agnostic as if they are choosing a lifelong political alignment. A recent perspective from the engineering team at GeekyAnts titled "Cloud-Native and Cloud-Agnostic Are Not Ideologies; They Are Business-Stage Decisions" cuts through this industry noise. Looking critically at their argument, it becomes clear that many organizations are suffering from premature architectural complexity. Engineering leaders frequently romanticize absolute portability long before their business has the operational maturity or the market validation to justify it. The core takeaway is simple yet profound: your architectural choice should be a reflection of your business stage, not a philosophical stance. The Go To Market Trap In the earliest stages of a business, the primary goal is not infinite scalability. The primary goal is survival. A startup needs to discover product market fit before running out of capital. This requires maximum release velocity, rapid experimentation, and minimum operational overhead. For an early stage company, leveraging a cloud native approach is entirely rational. Relying on managed databases, serverless functions, provider native identity management, and integrated monitoring allows a tiny engineering team to focus entirely on product features. The critical flaw in many early architecture reviews is treating this cloud dependency as a failure. It is actually a deliberate speed asset. At this stage, worrying about vendor lock in is a distraction because if you do not find customers quickly, there will be no vendor left to be locked into. Changing Priorities as the Business Matures The architecture that helps a company launch is rarely the one that sustains its long term growth. As a software product gains traction, the op