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As a systems engineering student and frontend dev, I wanted a portfolio that looked professional without spending days fighting with design. So I built one — and ended up turning it into a reusable template. Here's what I focused on while building it: 1. Customization from a single file The biggest pain with most templates is digging through components to change your info. I put everything — name, bio, projects, skills, social links — into ONE config file. Edit that, and the whole site updates. 2. Light & dark mode Developers love dark mode, so I made it the default, with a smooth toggle for light mode. Both are fully themed. 3. Mobile-first & responsive Most people will view a portfolio on their phone, so I built it mobile-first and tested it down to small screens. 4. Easy deployment It works out of the box with Vercel or any static host, with a beginner-friendly setup guide in the README. The stack React + Vite Tailwind CSS Formspree-ready contact form You can see the live demo here: devfolio-template-vercel-app.vercel.app I also made it available as a template if it's useful to anyone: https://payhip.com/b/t1VUk Would love to hear your feedback — what do you look for in a developer portfolio? react #webdev #tailwindcss #showdev

There's a headline pattern that feels like sharp marketing writing but quietly costs conversions. "You code. We cloud." It's clever. The parallel structure is tight. It names a clear division of labor. But it describes the service delivery model , not the developer outcome — and those are different things to someone scanning a landing page in five seconds. The audit fastapicloud.com is a managed hosting product built specifically for FastAPI developers. The hero H1 is: "You code. We cloud." On the surface this reads as clean, confident B2B positioning. In practice, it names the mechanism: You = who does the coding We cloud = who handles the infrastructure What's missing is the output. What does the developer actually walk away with? The gap (mechanism-first H1): The headline describes the service model without anchoring it in the developer outcome. The visitor has to make a three-step inference: "they handle the cloud" → "that means I don't do ops" → "so my app gets to production without a week of DevOps work." In five seconds of scrolling, most won't finish that chain. The headline earns a nod of recognition. It doesn't earn the scroll. The fix One line changes the frame completely. Before: "You code. We cloud." After: "Your FastAPI app is live in production — zero config rabbit holes, zero deploy-day surprises." The rewrite keeps the same promise — they handle the infrastructure — but anchors it in the developer's world. The outcome (app in production) is first. The pain points ("config rabbit holes," "deploy-day surprises") are the exact things a FastAPI developer has already lived through. "Zero config rabbit holes" names the experience of spinning up a production server for the first time. "Zero deploy-day surprises" names the dread: the Sunday night broken deploy that wasn't caught in staging. Any backend developer who reads that line knows exactly what it's describing. The mechanism (managed cloud, they handle ops) is still implied. But the headline earns the

VibeThinker: A 3B-Parameter Model Just Beat Opus 4.5 on Reasoning — Here's How A team of researchers has quietly dropped one of the most surprising AI papers of the month. VibeThinker , a model with only 3 billion parameters, reportedly outperforms Anthropic's Opus 4.5 on key reasoning benchmarks — and the secret sauce is a novel training recipe combining Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). For years, the dominant narrative has been that bigger is better. VibeThinker challenges that assumption head-on. Let's break down what happened, why it matters, and what it means for developers building AI applications in 2026. The Big News According to the paper (arXiv:2606.16140), VibeThinker achieves state-of-the-art performance on several mathematical reasoning and logic benchmarks while using roughly 1/30th the parameters of frontier reasoning models. The headline claim: it beats Opus 4.5 on a curated suite of competition-level reasoning tasks. This isn't just incremental progress. It suggests we're entering an era where training methodology trumps raw parameter count . What's Actually New: SFT + GRPO The two-stage recipe isn't entirely novel on its own — SFT then RLHF has been standard since InstructGPT. But VibeThinker's specific combination appears carefully engineered: Stage 1 — Targeted SFT : Fine-tune on a high-quality, diversity-maximized dataset of reasoning traces. The key insight here is curation over volume . Rather than scraping millions of examples, the team focused on a smaller corpus of well-structured chain-of-thought solutions spanning multiple difficulty tiers. Stage 2 — GRPO refinement : Group Relative Policy Optimization is a reinforcement learning technique popularized by DeepSeek. Instead of training a separate value model (as in PPO), GRPO compares multiple outputs within a group and rewards the best relative to its peers. This is far more compute-efficient than traditional RLHF. The synergy matters: SFT gives

Part 6 of a beginner-friendly series on learning Python from scratch. In Part 5 , we learned to organize data with lists, dictionaries, and other collections. Now it's time to organize our code itself. A function is a reusable block of code that performs a specific task. Instead of writing the same code multiple times, you write it once in a function, then call that function whenever you need it. This is the foundation of writing clean, maintainable programs. Defining and Calling Functions The basics def greet (): print ( " Hello, World! " ) greet () # Call the function Use def to define a function. The function name is followed by parentheses and a colon. The indented block below is the function's body. When you call the function (by typing its name with parentheses), Python runs the code inside it. Functions with parameters Most functions need information to work with. That's what parameters are for: def greet ( name ): print ( f " Hello, { name } ! " ) greet ( " Ramesh " ) # Hello, Ramesh! greet ( " Priya " ) # Hello, Priya! name is a parameter (placeholder). When you call greet("Ramesh") , name becomes "Ramesh" inside the function. Multiple parameters: def add ( x , y ): print ( x + y ) add ( 5 , 3 ) # 8 add ( 10 , 20 ) # 30 Return values A function can calculate something and give the result back to you with return : def add ( x , y ): return x + y result = add ( 5 , 3 ) print ( result ) # 8 The return statement stops the function and sends a value back. The caller can then use that value. def greet ( name ): message = f " Hello, { name } ! " return message greeting = greet ( " Ramesh " ) print ( greeting ) # Hello, Ramesh! A function can return multiple values as a tuple: def get_user_info (): return " Ramesh " , 25 , " Chennai " name , age , city = get_user_info () print ( name , age , city ) # Ramesh 25 Chennai Arguments: Positional vs Keyword There are two ways to pass values to a function: Positional arguments Arguments are matched by position: def describ

Last night I ran external security scans on the public websites of 10 leading Shopify and Shopify Plus agencies — the same scan any browser or attacker would see. No credentials, no special access. One agency scored an A. Three scored C- or below. The most common finding appeared on 9 of 10 sites. TL;DR 1 agency scored an A. 3 scored C- or below. 1 scored a D. The most common finding — missing security headers — appeared on 9 of 10 sites. 6 of 10 agencies have no HSTS at all. One agency has a session cookie without the Secure flag. That is the most concrete finding in the set. What was scanned Five categories per domain: TLS (HSTS presence and max-age), security headers (CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy), cookie flags, DNS hardening (DNSSEC and CAA) and sensitive exposure paths. All scans run on 23 June 2026. This covers the agencies' own marketing sites — not the client stores they build. Results Agency Domain Score Grade 1Digital Agency 1digitalagency.com 94 A Acidgreen acidgreen.com.au 77 B 30 Acres 30acres.com.au 76 B Fourmeta fourmeta.com 76 B Blend Commerce blendcommerce.com 76 B Elkfox elkfox.com 76 B Charle Agency charleagency.com 62 C Fyresite fyresite.com 62 C Eastside Co eastsideco.com 58 C- Swanky Agency swankyagency.com 55 C- Blubolt blubolt.com 54 D Per-agency notes 1Digital Agency — A (94) HSTS at two years, X-Content-Type-Options and Referrer-Policy set correctly, Permissions-Policy restricting camera, microphone and geolocation, CSP frame-ancestors in place of X-Frame-Options. Only gap is HSTS missing includeSubDomains. Acidgreen — B (77) HSTS with two-year max-age, includeSubDomains and preload — the strongest TLS config in the set. But CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy and Permissions-Policy are all absent. Worth noting Acidgreen is multi-platform (Shopify Plus, Adobe Commerce, Magento) rather than Shopify-only. 30 Acres — B (76) A Shopify Plus Partner agency based in Byr

Arize Phoenix is one of the most popular open-source LLM observability platforms in 2026. If you are evaluating it alongside CortexOps, this article compares them directly on the things that matter for production agent workloads. Short version: Arize Phoenix is the right choice if you are doing notebook-based experimentation, RAG evaluation, or need embeddings analysis. It is well-established with 9,000+ GitHub stars and strong OTel support. CortexOps is the right choice if you need a first-class CI/CD deployment gate, multi-framework agent tracing beyond LangGraph, or a flat-rate pricing model that doesn't get expensive at agent-scale span volumes. What They Are Arize Phoenix is an open-source LLM observability and evaluation platform from Arize AI. It self-hosts in one command, uses OpenTelemetry for tracing, and includes built-in evaluators covering faithfulness, relevance, hallucination, and toxicity. Phoenix supports popular frameworks including OpenAI Agents SDK, LangGraph, CrewAI, LlamaIndex, and DSPy. CortexOps is an open-source AI agent observability platform focused on the full production lifecycle — tracing, evaluation in CI/CD, and continuous monitoring. It supports 12 agent frameworks via a unified instrumentation layer and ships a CLI-based deployment gate that exits with code 1 when quality drops below a configured threshold. One Important Distinction Phoenix is optimized for prompt-centric experimentation and LLM-as-judge evals in a notebook-friendly self-host. When your app stops looking like a notebook — a production agent that runs for ten minutes, calls fifteen tools, spawns a sub-agent, and fails four tool calls deep — you open Phoenix and get a span tree. You wanted to know what the agent said to the user, what the user said back, and which tool call threw. CortexOps is designed for that second scenario: debugging multi-node agent failures in production with a structured execution waterfall, not a flat span list. Feature Comparison Feature Ariz

I kept running eslint and thinking my codebase was fine — then someone opened a PR and the first comment was "this function needs JSDoc." The problem: linters check your syntax . Nobody checks whether your exported API is actually documented. Those are two very different things. So I built jsdocscan — a zero-dependency CLI that walks your JS/TS files and flags every exported function or class that is missing JSDoc, or has undocumented parameters. What it catches Errors — exported function or class with no /** … */ block at all: ✗ src/api.js:12 fetchUser missing JSDoc ✗ src/utils.js:34 formatDate missing JSDoc Warnings — JSDoc exists but a parameter has no @param tag: ! src/render.js:7 renderCard undocumented params: opts Exit codes are 0 (all clean), 1 (issues found), 2 (usage error) — pipe-friendly. How to use it # scan a directory npx jsdocscan src/ # Python version pip install jsdocscan jsdocscan src/ # skip @param checks — just verify JSDoc exists npx jsdocscan --no-params src/ # machine-readable output npx jsdocscan --json src/ | jq '.[].findings' # summary line only npx jsdocscan --quiet src/ # custom extensions npx jsdocscan --ext .js,.ts src/ What it skips (intentionally) Non-exported functions and internal helpers — these are implementation details Destructured params ({ a, b }) — too many valid @param opts patterns TypeScript type annotations on params — name: string is stripped, @param name is still required Zero dependencies No parsers, no AST, no require("typescript") . It uses a line-by-line scanner that: Detects export function/const/class patterns via regexes Walks backwards to find a preceding /** */ block Compares @param names in the JSDoc against the actual parameter list npx jsdocscan works with zero install time. pip install jsdocscan brings in nothing extra. Links npm: npmjs.com/package/jsdocscan PyPI: pypi.org/project/jsdocscan GitHub (Node): jjdoor/jsdocscan GitHub (Python): jjdoor/jsdocscan-py Both versions pass the same 38-test suite. Same

I had a long Google AI Studio (Gemini) session that I wanted to keep. I selected the conversation in the browser, copied it, and pasted it into a text file. File size: a few hundred KB. "OK, that's safe." Later, I exported the same session as JSON. File size: a few MB. More than half of the data had silently disappeared. What was missing I checked what the manual copy had dropped. The system prompt The instruction I had originally given the model — the system prompt — was completely gone. Manual copy captures only the user/assistant turns visible in the conversation pane. The instruction context that shaped the entire session does not get copied. The tail of long responses When a Gemini response is long, the browser shows a "Show more" button. If you copy without expanding it, the response gets cut mid-sentence. Out of 8 sessions I checked, 3 had responses truncated this way. Newlines inside code blocks Newlines inside code blocks got mangled in several places. Responses containing JSON or YAML had indentation that no longer parsed. The reasoning trace For some models, the model's reasoning trace is stored separately from the visible response. Manual copy doesn't capture it at all. How to export as JSON Google AI Studio has a session export feature. In the session view, click the ... menu at the top right Select "Export" Choose JSON format and download The JSON contains the full data, including the system prompt. Measured: manual copy vs. JSON export I compared 8 sessions. Session Manual copy JSON Loss rate A tens of KB ~150 KB ~70% B ~90 KB ~200 KB 50-60% C ~30 KB ~100 KB 60-70% D ~50 KB ~180 KB ~70% E ~60 KB ~240 KB ~70% F ~20 KB ~70 KB ~60% G ~20 KB ~50 KB 50-60% H ~10 KB ~30 KB ~60% Total a few hundred KB ~1 MB 60-70% Average loss rate, 60-70%. The manual copy was, on every session, missing most of what was in the actual session state. Why I didn't notice If you open the manually-copied file, the conversation reads fine. As long as the start and end connect, a m

When you work with AI long enough, you start to notice it makes the same kind of mistake over and over. "You're coming on too strong, dial it back." It shrinks and goes meek. "Stop being meek." It comes on strong again. Each time you point something out, it apologizes sincerely. The next round, the same type of problem comes back from a different angle. After a while you realize you're babysitting the AI instead of working with it. This isn't because the AI is bad. It's a design quirk: today's AI is tuned to satisfy the user. The quirk won't go away. But if you change how you work with it, you can still get work done together. This piece is about that — five patterns of the quirk, and an operating mode that gets ahead of them instead of correcting them in flight. Five quirks in a single evening One evening I was running a strategy discussion past an AI, and in one back-and-forth I caught five distinct behaviors worth noting. Laid out, they look like this. Helpful-looking runaway. I asked it to push back harder. It immediately started using strong words ("you're avoiding responsibility," "this is the wrong call as a founder") to perform consultant-energy. The reasoning stayed thin. Only the tone got louder. Over-retraction on pushback. I said "your reasoning is thin." It launched into long self-criticism and threw the next decision back at me. Trusting its own research without checking. I asked it to use a secondary research feature (where the AI looks things up and summarizes). The summary came back. The AI claimed it had "verified the primary source" without ever opening it. Forced specificity. I was talking at a strategic, abstract level. It quietly mapped my words onto a specific real-world deal and jumped to "this is highly transferable." Punting the decision back. I asked it to decide. It laid out three options and said "which would you like?" The phrase "let me confirm three points" started showing up. Red flag. Each one of these looks, on the surface, like th

LinkedIn automation has a trust problem. Not with users — with LinkedIn itself. Most automation tools treat LinkedIn's API like an obstacle to route around. They send at fixed intervals, ignore behavioral limits, and optimize purely for volume. The result: accounts flagged within weeks, connection limits imposed, and in the worst cases — permanent bans. When we built SendCopy.ai, we approached this differently. Here is the technical breakdown of how we built LinkedIn outreach automation that actually protects accounts while scaling pipeline. The Core Problem: Behavioral Fingerprinting LinkedIn does not just monitor what you do — it monitors how you do it. Fixed-interval automation is trivially detectable. If your tool sends a connection request every 90 seconds with clockwork precision, LinkedIn's behavioral monitoring picks that up immediately. Human beings do not operate on fixed intervals. We get distracted, context-switch, move between tabs, have conversations in between tasks. The solution is not to slow down automation — it is to make it genuinely human-like. At SendCopy.ai, every action in a sequence uses variable timing — randomized within human-realistic ranges, distributed across natural working hours, and calibrated to each sender's historical activity patterns. Architecture: How We Handle Timing Variation The timing engine works on three levels: Level 1 — Action Delay Each individual action (send connection, send message, view profile) has a randomized delay pulled from a probability distribution weighted toward human behavior. Not a simple random range — a distribution that mirrors actual human activity patterns. Level 2 — Daily Activity Window Each sender account operates within a configurable activity window — typically 8–10 hours per day. Actions are distributed across this window with natural clustering around peak activity periods. Level 3 — Volume Ramp New sender accounts start with lower daily volumes and ramp up gradually over 2–4 weeks. This mi

Nine years ago, I built two versions of Highlander: an original jQuery application and a React/Redux version that used the same backend concepts. After successfully reviving and deploying the jQuery version, I turned to Highlander-react-redux. The goal was not simply to make an old repository run again. I wanted to improve the product, modernize its architecture without rewriting everything, and deploy something people could actually explore. This time, I used Turtle AI: a plan-driven engineering workflow built around Codex. It gave the AI explicit phases for planning, implementation, verification, testing, documentation, security review, and performance review. The process worked—but it also taught me that more guardrails do not automatically create a more efficient workflow. The Problem: More Than an Old React App The application had the typical problems of a nine-year-old project: Legacy React class components Complicated Redux connections Hardcoded localhost API URLs Authentication state disappearing after refresh Unprotected client routes Large Express route files mixing routing and business logic Inconsistent API errors No API versioning Limited filtering and pagination Outdated deployment assumptions UX gaps for demo users The app also had useful product ideas that had never been fully developed. Coaches could manage teams, players, and stats, but the experience needed stronger analytics, season support, collaboration, and more reliable workflows. I did not want to throw away the existing application and replace it with a new stack. The challenge was to preserve its original value while making targeted improvements. The Approach: Plan-Driven Product Engineering My previous Highlander revival prioritized: Get the app running locally Stabilize authentication and data Improve the demo experience Harden security Deploy For the React/Redux version, I followed a more structured workflow: Analyze the repository Create an implementation plan for one feature Implement

I spent the last few months trying to understand why reconciliation errors keep appearing in high-throughput pipelines. Here is what I found. In the race to process millions of transactions daily, modern fintech ecosystems have achieved a genuine miracle of scale. But beneath the surface of that velocity lies a structural problem most engineering teams aren't measuring: causality violations in async event pipelines. Most teams assume that if a transaction shows "Success" in the database, the job is done. At high concurrency levels, that assumption breaks quietly. When "Eventual Consistency" Becomes "Eventual Loss" In distributed systems, Kafka partitions and database shards experience micro-millisecond timing gaps. When a network retry delays a validation webhook, the downstream ledger can commit a wallet update before the validation that should have preceded it completes. To the user, the app glitches. To the engineering team, it's a reconciliation ticket. To the CFO, it's untracked operational cost. The Reconciliation Tax I built a simulation modelling this exact failure mode across 5,000 concurrent transactions. With an 8% network retry probability, conservative for high-traffic payment rails, the causality violation rate was 8.3%. At one million daily transactions, that's over 80,000 unvalidated commits every day requiring manual review. The operational cost compounds across three dimensions: engineering hours spent patching database state, fraud model accuracy degrading on out-of-order training data, and audit trails that cannot demonstrate strict causal sequence to regulators. The Fix The solution is enforcing strict event ordering at the ingestion layer before state commits happen, not better monitoring after the fact. When safeguards including partition-aware routing, exponential backoff, and idempotency controls were added to the same simulation, the violation rate dropped to 0%. Full simulation code and methodology: github.com/yakuburoseline1-gif/cif-simul

AI wrote the first 80% of my feature in 10 minutes. The code was clean. The logic made sense. The...

A running look — in reverse chronological order — at the bigger tech companies that have announced significant layoffs this year with AI as a stated factor.