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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

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

An open-source security guide, compliance checklist, and LLM-based virtual CISO persona for startups -- with specialized coverage for foreign companies entering the Korean market. The Problem Startups are vulnerable. Limited resources, no dedicated CISO, and security always deferred to "later." But customer data and intellectual property accumulate from day one -- and legal obligations apply regardless of company size. Three incidents from Korea in the first half of 2026 demonstrate that one misconfiguration can cascade into existential damage: Tving Data Breach (2026.06): Mass exposure of CI (Connecting Information, Korea's digital identity key) and refund bank account numbers. Classified as a "major breach" by the Personal Information Protection Commission. The leaked CI enables cross-service identity correlation, multiplying the damage. (CTI-2026-0604-TVING) CU Convenience Store Delivery Hack (2026.06): A simple web vulnerability led to the exfiltration of CI, addresses, phone numbers, and 9+ other data fields. The leaked data was linked to illegal private investigator inquiries and secondary crimes. (CTI-2026-0604-CU_BREACH) FastCampus / DayOne Company GitHub Master Key Theft (2026.06): A single GitHub master key was exfiltrated, granting attackers 30 days of undetected access to internal systems. Over 700,000 user records were exposed. The company took approximately 30 days to detect the breach, and customer notification was delayed beyond 72 hours. (CTI-2026-0611-FASTCAMPUS_DAYONECOMPANY) The common thread: All three began with a single misconfiguration or a single overlooked vulnerability. None required sophisticated zero-days. The damage was inversely proportional to organizational maturity. What startups need is not a $100K security suite. It is knowing what to do first , and a system to check it regularly . The Core Hypothesis An LLM can serve as a startup's first CISO. As of mid-2026, Claude 4, GPT-4o, and DeepSeek V3 -- alongside locally-run models via O

The Playwright Playbook — Part 3: Multi-User, Multi-Tab & Browser Context Testing "Most frameworks test one user at a time. Real apps don't work that way." In Part 1, we built the full foundation — POM, storageState , fixtures, and a clean project structure. In Part 2, we took control of the network layer — mocking APIs, simulating failures, and asserting on real network calls. Now we tackle the scenario that breaks most automation frameworks. Multiple users. Simultaneously. In one test. Think about your app for a moment. How many features actually involve just one user? Admin assigns a task → regular user sees it appear in real time User A edits a record → User B gets a notification Admin revokes access → User's session should invalidate Two users try to edit the same record → conflict resolution kicks in These are real features. They need to be tested. And page.goto('/login') in a beforeEach won't get you there. Playwright's browser context architecture was built exactly for this. Let's use it properly. 🎯 🏗️ Where We Left Off After Part 2, our project looks like this: playwright-playbook/ ├── tests/ │ ├── auth/ │ │ └── login.spec.ts ✅ Part 1 │ ├── tasks/ │ │ └── task-management.spec.ts ✅ Part 1 │ └── network/ ✅ Part 2 │ ├── api-mocking.spec.ts │ ├── error-simulation.spec.ts │ └── network-assertions.spec.ts ├── pages/ │ ├── LoginPage.ts ✅ Part 1 │ └── TaskPage.ts ✅ Part 1 ├── fixtures/ │ ├── auth.fixture.ts ✅ Part 1 │ ├── tasks.json ✅ Part 2 │ ├── empty-tasks.json ✅ Part 2 │ └── tasks-har.har ✅ Part 2 ├── scripts/ │ └── record-har.ts ✅ Part 2 ├── .auth/ │ ├── admin.json │ └── user.json ├── global-setup.ts ✅ Part 1 ├── playwright.config.ts ✅ Part 1 └── .env By the end of Part 3, we add: playwright-playbook/ ├── tests/ │ ├── multi-user/ ← NEW │ │ ├── role-permissions.spec.ts │ │ └── realtime-collaboration.spec.ts │ └── multi-tab/ ← NEW │ └── multi-tab-flows.spec.ts ├── pages/ │ └── DashboardPage.ts ← NEW ├── fixtures/ │ └── multi-user.fixture.ts ← NEW Every one of th

This is a submission for the June Solstice Game Jam What I Built My game, Sunny Up, celebrates International Sushi Day! I intended to make a cute casual game because who doesn't like simple and cute things, right? I was inspired by games I liked in the 2010s that were simple, fun, and strangely addictive. The game's theme also has a lot to do with the solstice, which can be observed in the four scenarios where the game makes reference to it. Video Demo Code maikpalharoni1 / sunny-up my game sunny up for june solstice game jam sunny-up my game sunny up for june solstice game jam View on GitHub How I Built It Fun fact: I started brainstorming the idea while I was working on another game, a cute virtual pet (which even celebrates a traditional festival in my country in June, called Festa Junina), and in doing so I became obsessed with the stories of virtual pet developers from the 2010s. I mainly read about Paul Salameh and his game Pou, and also about Rovio with Angry Birds, which were my favorite stories. I combined two mechanics from both games, and that's where I decided how the game would work. I'm a noble pioneer of HTML, CSS, and JS, so I used AI to give me greater support, optimization, and help me write the code in parts of the project. I'm not a designer, nor can I afford one. I used Gemini AI for the game's graphics, acting as an art director to ensure everything came out as best as possible, removing the AI ​​sloppy aspect and giving the project more life. Prize Category I really liked the artwork with Gemini and the emotional support and evaluation of Google's AI mode, so I think it's worth considering for the Best Use of Google AI category, even though I'm not aiming for any specific category. Play the game Online in browser on Itch.io: https://coffee-calf.itch.io/sunny-up Thank you for reading, it was fun participating in the challenge!

"An AI agent just built a production landing page, with GDPR audit logs and encryption baked in. I wasn't even at my desk." That is not a lucky one-shot. It is a repeatable workflow. Piotr Karwatka recorded a full tutorial showing how to go from idea to a production-ready app on Open Mercato - the AI-Engineering Foundation Framework for CRM/ERP - with no babysitting and no ping-pong prompting. This is the technical version: what the loop actually looks like, why it doesn't fall apart, and which patterns you can lift into your own stack. The problem with conversational coding The default AI coding loop is single-threaded and human-bound: prompt -> generate -> you spot a bug -> correct -> re-prompt -> repeat It holds for snippets. It collapses the moment the task touches real architecture - multi-tenancy, RBAC, event flow, encryption, audit logging. Corrections pile up in the context window, the agent loses the thread, and you are back to typing. You are the bottleneck, sitting in the inner loop. The workflow in the tutorial moves you to the outer loop : you review a finished, tested PR instead of every keystroke. goal -> agent: branch + implement + test + open PR -> you: review PR The reason this is even possible on Open Mercato is that the hard architectural decisions are already encoded as conventions, specs and agent-readable skills ( AGENTS.md , task routing, spec skills). The agent is not inventing how RBAC or GDPR logging should work - it reads the foundation and follows it. 1. Fire-and-forget: the autonomous PR loop The execution agent owns the full unit of work: 1. git checkout -b feat/lead-capture-landing 2. implement against framework conventions 3. run the test suite (Playwright integration tests included) 4. open a structured PR: what changed, why, how it was verified You are no longer correcting tokens. The deliverable is a reviewable artifact. In the tutorial the output is concrete: a live site capturing leads straight into the Open Mercato CRM, with GD

Playwright is the best browser automation library in 2026. It's also the most fingerprinted, the most detected, and the most patched in anti-bot databases. If you're running default Playwright against any platform with serious anti-bot defenses, you're getting flagged. We learned what's left of the cat-and-mouse game the hard way building HelperX . This article is what still trips up Playwright today — beyond the well-known navigator.webdriver flag, which everyone fixes in week one. The detection surface has shifted dramatically since 2022. The classic tells (PhantomJS, missing plugins, undefined chrome object) are largely solved. The new battleground is more subtle: CDP protocol artifacts, behavioral inconsistencies, and side-effects of the Playwright runtime that aren't part of the public API. What stopped working in 2024-2025 Detection on the platforms we monitor has gotten dramatically better in the last 18 months. A few things that worked in 2023 and stopped working since: Generic navigator.webdriver = undefined patches — detectable via Object.getOwnPropertyDescriptor if you do it naively Patching Notification.permission — modern detection cross-references with Permissions.query() Faking window.chrome — the property structure changed; old fakes are missing newer subkeys Setting a single User-Agent profile — detection now expects Client Hints to match Empty navigator.languages — flagged as suspicious; needs at least 2 entries These were all "set it once, ship it" fixes. The current generation of detection requires ongoing engineering. CDP artifacts: the deepest tell The Chrome DevTools Protocol (CDP) is how Playwright controls the browser. The browser exposes signals that it's being controlled, and modern detection looks for them specifically. Symptom: Runtime.evaluate artifacts When you run await page.evaluate(() => ...) , Playwright uses Runtime.evaluate under the hood. The evaluated script runs in a special isolated world. If the page can detect that an isola

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In this blog post, we will see how I use Qwen Code's slash commands and workflow strategies to build Achu my screenshot beautifier app without burning through tokens or losing context mid-session. If you haven't heard of Achu , it's a desktop app built with Electron + React + TypeScript. It does screenshot beautification, Privacy Guard (offline OCR redaction), Auto-Vibe (palette-extracted backgrounds), and an AI Bug Agent with GitHub integration. It's a side project I'm genuinely proud of, and Qwen Code has become my go-to agentic coding CLI for it. A developer shares their day-to-day workflow for using Qwen Code, an open-source agentic coding CLI, to build Achu, a desktop screenshot beautification app built with Electron, React, and TypeScript. The post covers how slash commands like /init, /plan, /compress, /remember, and /btw are used to manage context, reduce token costs, and maintain consistent output across sessions. The core approach centers on spec-driven planning through iterative /plan sessions before any code is written, combined with parallel subagents for independent tasks and strict context hygiene using /compress and /clear. Additional practices include pointing the model at library source code instead of documentation and using /remember to persist architectural decisions across sessions. This isn't a tutorial about what Qwen Code is. It's about how I actually use it day-to-day, the slash command tricks I rely on, and the discipline it takes to get real work done with an LLM in a terminal. It all started with Google Antigravity, but the 5 hours reset and weekly limits is killing my productivity and thinking flow. I had to switch to more affordable and open source model where I chose Qwen. Why Qwen Code? I've tried Claude Code, Gemini CLI, and a bunch of others. Qwen Code is open source, has excellent subagent support, a rich slash command system, and Qwen Max is genuinely strong at reasoning through complex TypeScript and Electron internals. My go-to

🚀 Technical Briefing: This tutorial is part of our deep-dive series on Agentic Workflows at Gate of AI . For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the original article here . <span>Tutorial</span> <span>Advanced</span> <span>⏱ 45 min read</span> <span>© Gate of AI 2026-06-16</span> Learn how to fine-tune large language models (LLMs) to enhance communication capabilities in specialized domains, such as homeless shelters, using modern AI tools and techniques like LoRA. Prerequisites Python 3.10+ OpenAI API key (latest version) Familiarity with machine learning concepts What We're Building In this tutorial, we will embark on a journey to fine-tune a large language model (LLM) to cater to the specific communication needs of homeless shelters. By leveraging a bespoke dataset compiled from the Youth Spirit Artworks (YSA) Tiny House Empowerment Village website, we aim to create a model that can effectively assist in the nuances of communication required in such environments. The finished project will result in a model capable of generating contextually relevant and empathetic responses to inquiries typical within the homeless shelter community. This involves structuring data into a standardized question-and-answer format to enhance the training process, ensuring the model's outputs are aligned with the communication style and needs of the target audience. Setup and Installation To begin, we need to set up our development environment with the necessary tools and libraries for model fine-tuning. We'll be using Python along with the OpenAI library to interact with the LLMs. pip install openai pandas numpy Additionally, you'll need to configure environment variables to securely store your API keys. This ensures that sensitive information is not hardcoded into your scripts. .env file OPENAI_API_KEY=your_openai_api_key Step 1: Data Collection and Preparation The first step in fine-tuning our model involves collecting and

Coding agents draft specs, architecture docs, changelogs, and README updates in seconds — but a human still has to judge the quality of all that output. The bottleneck shift A year ago, the typical workflow was: you write a spec, you get comments, you revise, then you implement and get code review. Humans did most of the writing and coding. Now, agents produce first drafts of design docs, API references, runbooks, and onboarding guides — and they do it in seconds. Code implementation and code review can now be handled by agents, so those are no longer the bottleneck. What surfaced instead is the step right before: document review. A human has to read 2,000 lines of generated markdown and decide what's wrong. The writing part got dramatically faster. LLMs can assist with document review too, but compared to code implementation and code review, the human judgment required is still larger. This asymmetry compounds fast. Every agent-assisted project now has a stack of "needs human review" documents growing in a shared folder. If you're running multiple agent loops in parallel — one for the spec, one for the implementation plan, one for the test strategy — review becomes a pipeline stall. GitHub PRs remain the right tool when you need third-party review. But the step before that — the fast local self-review loop where you and your agent iterate on a draft — doesn't belong in a PR. Branching, diffing, and assigning reviewers is a lot of process for a first draft the agent wrote in seconds. Why prose feedback is lossy The most common workaround today is to have the agent read the document and then fix things based on natural-language feedback: "The error handling in section 3.2 is too vague — be specific about what happens on timeout." This looks reasonable. The agent reads it, searches for something about error handling, and makes a change. But several things go wrong: Position is ambiguous. If section 3.2 has three paragraphs about error handling, which one did the revie

The deployment should have taken a few minutes. The application was running, DNS was configured correctly, and the domain was already pointing to the server's public IP. Caddy was configured as a reverse proxy and was listening on ports 80 and 443. Every item on my deployment checklist appeared healthy. Yet every Let's Encrypt validation attempt kept failing. The error looked simple enough: authorization failed timeout during connect likely firewall problem At first, I believed it. I checked DNS resolution, verified firewall rules, confirmed that Caddy was listening on the expected ports, and made sure the application itself was reachable. Every check came back clean. That was the first clue that the problem might not be where the logs were pointing. The Obvious Things The first assumption was DNS. I verified that the domain resolved to the correct public IP. dig +short my-domain.com Everything looked correct. Next came the firewall. sudo ufw status Ports 80 and 443 were open. There were no unexpected deny rules, and nothing suggested inbound traffic was being blocked. Then I checked whether Caddy was actually listening. sudo ss -tulpn | grep -E ':80|:443' Again, everything looked normal. The application itself was healthy too. curl http://localhost:3001 returned a valid response. At this point I had checked most of the things engineers typically check when certificate validation fails. DNS looked good, the firewall looked good, the reverse proxy was healthy, and the application was running. Yet the validation errors continued. The Part That Sent Me In The Wrong Direction The error messages kept mentioning connectivity problems and possible firewall issues. That wording influenced my thinking more than it should have. I spent time investigating firewall rules, reverse proxy configuration, TLS settings, and domain configuration. Every new hypothesis felt reasonable, but none of them explained why local tests consistently succeeded while external validation continued