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Hey, Everyone hope so you all are doing great. So, My Name is Arslan. I am a IT Student and i love to get to develop or find best alternative solutions that can solve problems. So, i have a used pc last year at which i was working on a small college project. so, i was very frustrated with errors so i search for error tracking software and tools but when i search and get to know about these heavy tools and expensive tools i thought let's build my own lightweight fast error tracker tool. So, i collected my money for about 8 months to buy a used laptop because my current pc was a potato old pc causing problems. So, i decided to do something unique. So, i sell my pc and take my collected money to get a used laptop. So, i get my laptop and then started working on this project. So, as a solo developer i worked for months to make this tool and now finally this tool is here but i have kept this tool under beta development and it is still under upgradation. I just want your useful feedback and honest suggestion and support. Join my journey by using this tool and catching your errors in a snap because it is snap trace. bye.

Six coding agents were running. I could not tell you what any of them had done. Not roughly. Not approximately. The output was there, the files had changed, and the honest answer to "which one did that" was a shrug. Three questions in particular had no answer: which run burned the tokens, whether they genuinely ran at the same time or merely started together, and whether two of them had quietly edited the same file. That last one is the expensive question. An agent working on the wrong file looks exactly like an agent working on the right one, right up until you read the diff. The thing that was already true Every one of those runners writes a transcript to disk while it works. Claude Code does. So do Cursor, Codex, Gemini CLI, Copilot CLI and Kiro. The record of what happened was sitting in my home directory the entire time, in six different formats, none of which I had ever looked at. So runlanes does not wrap anything. There is no SDK, no instrumentation step, no account, and nothing to start before the run starts. It reads what the runner already wrote. The consequence is the part I did not expect to matter as much as it does: it works on runs that already finished. Most tools in this space need you to have decided, in advance, that this particular run was worth watching. This one can answer a question you only thought to ask afterwards. npx runlanes That opens a console on 127.0.0.1:4180 for whatever project you are standing in. There is no configuration file to write first. What it actually shows Now is every live session, across every runner it found, with what the main conversation spent against what it handed to subagents. On the session that motivated the whole thing, that split was 8.3 million tokens of conversation against 2.1 million delegated, which was not the ratio I would have guessed. The parallelism figure is the one I keep coming back to. Peak concurrency was four agents. The share of elapsed time where anything genuinely overlapped was 9% . Four

In the fast-paced world of software development, every second counts. Seamless tool interaction is not just a convenience; it's the bedrock of high developer productivity . Even seemingly minor hitches, like a non-functional 'copy link' button, can subtly erode efficiency, leading to frustration and lost time. A recent GitHub Community discussion highlighted just such an issue, where a user reported that the 'Copy link' button in Pull Requests (PRs) was consistently failing, specifically when using the Arc browser on macOS. This isn't merely about a broken button; it's a window into the complex interplay between browsers, web APIs, and the essential tools we rely on daily. The Reported Problem: A Month-Long Frustration The original post by vovapyc detailed a persistent problem: the 'Copy link' button in GitHub PRs had been broken for at least a month. The user specified their setup: Arc browser, MacBook Pro M1 Pro, and macOS 26.2. For dev teams, product managers, and delivery leads, a recurring point of friction like this, preventing a quick share of a PR link, represents a tangible drag on workflow. Imagine the cumulative time lost across a team if every developer had to manually copy URLs from the address bar multiple times a day. GitHub's automated response, while a standard and necessary part of their feedback loop, acknowledged the feedback and assured the user that their input would be reviewed. However, it didn't immediately offer a solution or explanation for the bug, leaving the user, and potentially others experiencing similar issues, in limbo. Diagram illustrating the three gates: Secure Context, Document Focus, and User Permission, that must be passed for the Clipboard API to function.## The Expert Insight: It's Likely the Browser, Not GitHub The true insight, and the crux of this discussion, arrived from hoangperry . Their comprehensive breakdown suggested that the issue was almost certainly browser-specific rather than a core GitHub bug. This distincti

Microsoft opened GitHub Copilot code review for Azure Repos to all Azure DevOps customers, after acknowledging that many are not ready to migrate to GitHub. Reviews bill per use through the linked Azure subscription and appear in Cost Management 48 hours later. Budgets notify but do not stop reviews, and concurrency caps at five per organization. By Steef-Jan Wiggers

If you are an active user of AI-powered IDEs like Cursor, VS Code with Copilot, or Windsurf, you already know the sinking feeling of seeing this notification: "You have used 100% of your fast premium requests for this billing cycle." Suddenly, your snappy, context-aware coding assistant slows to a crawl or starts racking up expensive pay-as-you-go API bills. At the same time, you are likely paying $20/month for a ChatGPT Plus or Team subscription that sits underutilized in a browser tab. You use it for general questions, but it lacks direct, real-time access to your local codebase, forcing you to engage in a tedious dance of copying and pasting code blocks. What if you could bridge this gap? What if you could let ChatGPT Web do the heavy reasoning and planning using your local context, while saving your premium IDE tokens for fast auto-completions ? In this article, we’ll explore a highly novel, intermediate-level setup that does exactly this. By leveraging the Model Context Protocol (MCP) , Node.js , and secure Cloudflare Tunnels , you can route heavy code-planning tasks directly to your web-based ChatGPT Plus subscription safely and completely free of extra token charges. The Philosophy: Let ChatGPT Think, Let Your IDE Work When building complex software with AI, your workflow generally splits into two distinct phases: Reasoning & Planning (High Token Usage): This is where you ask the AI to read 10 source files, understand the architecture, design a new feature, or find a subtle bug. This consumes massive amounts of context window tokens. Execution & Autocomplete (Low Latency): This is where the AI writes single lines of code, refactors a function, or autocompletes your imports. This requires fast, inline API queries. Paying premium API rates (per token) for Phase 1 is incredibly expensive. This is where this open-source MCP bridge project shines. It exposes a read-only view of your local project as an MCP server. Your web-based ChatGPT (via custom GPTs or MCP int

Last summer I built an AI chatbot almost entirely in Claude Code. It worked. I never pushed it to GitHub. I felt that putting my name on a public repo felt like making a claim I couldn't back up. There is a particular kind of quiet that follows building something you don't feel entitled to. No matter how rewarding the project feels, somewhere behind your ribs a voice says: you didn't actually do that. If you've felt it, you already know the argument I'm about to make against. The stigma, stated fairly The criticism deserves better than a strawman, so here it is at full strength. Skill comes from struggle. When you sit with a bug for three hours, you're not just fixing the bug - you're building a mental index of how this kind of thing breaks. The frustration is the encoding mechanism. Hand the struggle to a model and you get the fix without the index. Do that a thousand times and you've shipped a thousand features while learning almost nothing, and you won't find out until the day the model is wrong and you have no idea it's wrong. There's a second, harsher version: that AI-dependent developers are pricing themselves as engineers while functioning as typists, and the industry hasn't caught up yet. I think both of these are pointing at something real. I just think they've misidentified the cause. The real failure mode Here's the honest part, and I want to say it before the defense, because a defense that skips it isn't worth much. AI absolutely can make you worse. I've watched it happen, and I've done it. The mechanism is specific: you accept output you haven't read. That's it. That's the whole failure. Not "using AI" - accepting without reading. It's seductive because it works. The code runs. Nothing punishes you. You get a small hit of progress and you move on, and the debt is invisible because the thing you failed to learn doesn't announce itself. You only meet it later, usually at 11pm, when something breaks in a layer you never looked at. A developer in that loop

What a "production-ready" FastAPI starter actually needs Every FastAPI project I've started begins the same way: an hour of boilerplate before I write a single line of actual logic. Auth. A database session dependency. A folder structure that won't fall apart once there's more than one resource. A test setup that doesn't take longer to configure than the tests themselves. I got tired of rebuilding it, so I built it once, properly, and wrote down why each piece is shaped the way it is. The structure Every resource in the project follows the same four layers: Router — HTTP in/out only. Parses the request, calls a service, serializes the response. No business logic lives here. Service — business rules. Ownership checks, "does this already exist" decisions, orchestration. No FastAPI imports — this layer doesn't know it's running inside a web framework. Repository — persistence only. SELECT/INSERT/UPDATE/DELETE via SQLAlchemy. No business rules. Schema — Pydantic models for request/response shapes, kept separate from the ORM models. This feels like overkill for a single resource. It stops feeling that way the first time you need the same ownership check enforced in two different routes, or the first time you want to unit-test a business rule without spinning up the whole ASGI app to do it. The decisions that actually mattered Testing against real Postgres, not SQLite. A SQLite-backed test suite gives you false confidence — native UUID types, enum handling, and constraint behavior all differ enough that "tests pass" stops meaning "the Postgres-specific code works." Each test runs inside a SAVEPOINT that gets rolled back afterward, so isolation doesn't cost a schema rebuild per test. Two token types, not one. Short-lived access tokens (15 min) plus longer-lived refresh tokens (30 days), with the token's type claim checked on every decode — a refresh token presented where an access token is expected gets rejected on that alone, not just on signature validity. One error shap

Claude Fable 5.1 is officially available in the Model Garden on Agent Platform. Built for long-running, high-stakes work, Fable 5.1 puts frontier intelligence into production across your code, documents, and research. 👉 Try it today and let us know what you're building: Claude Fable 5.1

Google is setting new memory-use limits for Android apps as AI data centers contribute to hardware shortages that could leave lower-cost phones with less memory.

Tired of Claude Code generating bizarre, overly dramatic jargon like "load-bearing spine"? You can fix this by enforcing Simplified Technical English (STE) in your system instructions or .claudemd files. This 1970s aerospace standard restricts vocabulary, forcing your AI agent to communicate in clear, direct, and highly actionable prose. "The load-bearing spine has hit a ceiling, and that is a significant foot gun with a large blast radius." If you have spent any time recently working with AI coding agents, you have probably stared at your terminal reading absolute gibberish like this, wondering: What on earth are you trying to tell me? I asked a straightforward technical question, and instead of a direct answer, I got a theatrical performance. It is incredibly tiring to translate AI metaphors back into plain English just to figure out which line of code actually broke. Fortunately, there is a remarkably elegant fix for this. The solution does not involve complex prompt engineering; instead, it leverages a fifty-year-old aerospace standard: Simplified Technical English (STE) . Why does Claude Code output weird technical jargon? AI models generate overly dramatic jargon because they are trained on vast internet corpuses where technical writing is often cluttered, metaphorical, and performative. To sound authoritative, the model indexes on complex vocabulary and metaphorical hand-waving instead of simple, direct statements. Imagine a scenario where your team is debugging a database lock. A human engineer would say, "The transaction is blocked." An AI model, eager to please and sound sophisticated, might describe it as a "temporal execution bottleneck causing systemic architectural paralysis." This happens because reinforcement learning from human feedback (RLHF) often rewards models for sounding smart and comprehensive. Without strict stylistic constraints, the agent defaults to verbose, metaphorical explanations that add cognitive load rather than solving your proble

A technically perfect app can still fail. It can have clean code, modern architecture, powerful APIs, and impressive features—and still leave users uninstalling it, abandoning transactions, or switching to a competitor. Because users don't experience your code.They experience the product. That is why developers and businesses need to look beyond functionality and ask a more important question: “Does this software make the user’s life easier?” The Real Cost of a Poor Digital Experience Customer expectations are rising quickly. According to PwC’s 2025 Customer Experience Survey, 70% of executives say customer expectations are evolving faster than their companies can adapt. Even more importantly, 29% of consumers said they stopped using or buying from a brand because of poor customer experience. That means a frustrating digital experience isn't simply a UX problem. It can become a business problem . A confusing checkout flow, slow screen, unnecessary registration step, broken search function, or poorly designed notification can turn a potential customer into a lost customer. And users rarely tell you exactly what went wrong. They simply leave. More Features Don't Always Mean More Value One of the biggest mistakes in software development is assuming that adding more features automatically makes a product better. It doesn't. Imagine an app with: 30+ features AI integration Multiple dashboards Complex personalization Advanced analytics …but users struggle to complete the one task they downloaded the app for. That's not innovation. That's friction. A better development approach starts with identifying the core user problem and then building around it. Before adding a feature, ask: What problem does this solve? If the answer isn't clear, the feature may not belong in the product. Performance Is Part of User Experience Developers often separate performance from UX. Users don't. To them, a slow API, delayed screen, frozen button, or failed transaction is simply a bad experien

X has sent cease-and-desist letters to Nitter, the open source project behind privacy-friendly X frontends, demanding its instances and code repository be taken down over alleged scraping.

I started my career as a software developer, Initially a front end developer and then became a full stack developer where success often meant solving difficult technical problems, building reliable systems, and delivering good software. Becoming a co-founder changed that perspective. Suddenly, building a product wasn't just about writing code. It was about understanding the problem deeply, making decisions with incomplete information, taking responsibility for outcomes, building a team, and constantly deciding what not to build. Now, as an Engineering Director at an AI company, I'm learning to balance both sides staying close to technology while thinking about people, product, strategy, and long-term engineering decisions. Honestly, The transition from developer to founder hasn't been a straight line. It's been a continuous process of learning, unlearning, and becoming comfortable with uncertainty. I'm starting this blog to document some of those lessons from building AI products and engineering teams to the technical decisions and challenges that come with growing a technology company. I know I'm just beginning my journey and that I thought I could perhaps share it with my tech community.

The Remote Job Search Playbook for Developers Outside the US/EU Remote work opened the door for developers outside major tech hubs to compete for roles that used to be geographically gated. It also created a much bigger applicant pool for every posting. If you're searching from outside the US/EU, here's what actually affects your odds — beyond "just apply to more jobs." Timezone overlap is a real filter, not a footnote A lot of "remote, worldwide" postings quietly mean "remote, but we need 4+ hours of overlap with our core team." Before you apply, check what timezone the company or their existing team is in. If you can genuinely offer a workable overlap, say so explicitly in your application — don't make a recruiter guess whether a 7-9 hour time difference is going to be a problem later. Sourcing channels that actually produce interviews Recruiting-as-a-service platforms (Rightfit-style agencies, Toptal, Turing) — they pre-filter for companies actively hiring remote and internationally, which saves you from applying into a black hole on a generic job board. Company engineering blogs and changelogs — companies that write publicly about their engineering tend to also be more remote-mature and less nervous about hiring outside their home country. Referrals inside communities you're already part of — dev.to, Discord servers for your stack, open-source project maintainers. A referral skips the "will this person actually work out remotely" anxiety that a cold application can't answer. Direct outreach to smaller, funded startups — they often can't afford local senior talent and are more open to global hiring than enterprise companies with rigid HR policy. What to lead with in your application Recruiters hiring internationally are quietly screening for risk: will this person disappear, will communication be a problem, will payment/compliance be a headache. Address these before they have to ask: State your availability and overlap hours plainly. Link to async-friendly proof

skillcheck is a static analyzer for SKILL.md files, the format agents like Claude Code, Copilot, Codex, and Cursor use to load reusable skills. It validates frontmatter, scores description discoverability, checks file references, enforces token budgets, and flags cross-agent compatibility issues. No network calls, no LLM calls, no file mutations. Runs as a CLI, a GitHub Action, or a pre-commit hook. pip install skillcheck skillcheck skills/ Latest pass was hardening and accuracy, not features. Here's what changed and why. Description scores went up. Skills that were scoring low because the scorer was broken will now see a jump in scoring. Median across the reference corpus went from 75 to 90. --explain-score also now tells you which pattern hits or misses instead of just a number. The score exists to predict whether an agent will actually find and trigger your skill, so a scorer that under-credits good descriptions defeats the point. The fix was validated against real-world skills, and the separation held: filler still scores 28-65, well-written descriptions 85-100. Corrupt files now fail cleanly instead of crashing. Before, a bad history ledger or non-UTF-8 skillcheck.toml above the skill dumped a Python traceback. It's now a clear error naming the file and byte offset (exit code 2). Config discovery walks up the directory tree, so one bad file could break every scan under it. Now every untrusted read (ingest, history, config) goes through the same guard before parsing, so they all reject the same way. README has been corrected in regards to token estimates. Without tiktoken, expect roughly 20-30% over-estimation, so install the extra if you're near a budget limit. The offline heuristic feeds the budget checks and its accuracy had never actually been measured, just assumed. It's benchmarked against tiktoken across the full corpus now, and the documented numbers are the measured ones. pip install "skillcheck[tiktoken]" The rest of the pass is invisible on purpose: f

Microsoft has made the Azure DevOps Remote MCP Server generally available, offering a hosted endpoint into work items, repos, and pipelines with nothing to install. Claude Desktop, Claude Code, ChatGPT, and Cursor cannot connect yet because Entra lacks support for dynamic client registration and Client ID Metadata Documents. By Steef-Jan Wiggers

Photo by Microsoft Copilot on Unsplash TL;DR: In a poll of 138 developers, three‑quarters say Claude Code outperforms Codex for everyday AI‑driven coding, pointing to higher accuracy, deeper context awareness, and a smoother workflow. The AI‑coding battlefield has been dominated by OpenAI’s Codex for years, powering tools like GitHub Copilot and shaping how developers write code. Yet a fresh wave of feedback suggests a shift: Anthropic’s Claude Code is rapidly becoming the preferred assistant for many programmers. A recent survey of 138 software engineers—spanning startups, enterprise teams, and freelance coders—revealed that 75% now rely on Claude Code as their go‑to AI partner. What drives this migration, and what does it mean for the future of AI‑augmented development? Survey Overview and Key Findings The questionnaire targeted developers who regularly use AI code generators, asking them to rank their primary tool and rate specific workflow attributes. Respondents represented a broad skill spectrum, from junior developers to senior architects, and worked across languages such as Python, JavaScript, Java, and Go. Adoption rate: 104 out of 138 participants (75%) listed Claude Code as their primary AI assistant, while only 34 (25%) still favored Codex. Primary criteria: Accuracy of generated snippets, ability to retain long‑form context, and ease of integration into existing IDEs topped the list. Secondary factors: Cost efficiency, response latency, and the perceived safety of the model (fewer hallucinations) also swayed decisions. The data paints a clear picture: developers are no longer satisfied with a one‑size‑fits‑all approach. They want an AI that can understand the nuance of a multi‑file project, stay on‑topic across extended sessions, and deliver code that compiles on the first try. Why Claude Code Wins Over Codex Higher Accuracy and Fewer Hallucinations Respondents repeatedly highlighted Claude Code’s ability to generate syntactically correct, production‑re

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Chat is great for intent, but agent work gets lost in the scroll. Here is how I use canvases with my agentic workflows—and why your workflow also deserves a canvas. The post How canvases make agentic workflows visible, steerable, and cost-efficient appeared first on The GitHub Blog .

Expedia Group has open-sourced mockql-rs, a Rust CLI that fills @mock-annotated GraphQL fields with LLM-generated data at request time. It follows Airbnb's @generateMock in April and a GraphQL Foundation RFC opened in February. All three solve the same problem with different architectures, and two use the same directive name with incompatible semantics. By Steef-Jan Wiggers