标签：#developer

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

Documentation OPEN SOURCE official documentation content, searchable and organized ...

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

The term software factory is getting a lot of attention right now, and for a good reason. AI coding assistants can generate code much faster than before. But faster coding alone does not mean faster, safer delivery. In many teams, it simply moves the bottleneck to review, testing, deployment, and operations. A software factory is a way to organize the entire software development life cycle as one connected, repeatable system. Think of a car manufacturing assembly line. Each station has a clear job, work moves forward in a predictable order, quality checks happen at the right moments, and the finished product is inspected before it leaves the factory. An agentic software factory applies that same idea to software delivery. AI agents do focused work across planning, coding, testing, deployment, monitoring, and feedback. Humans remain in charge of specifications, security, policies, approvals, and the decisions that should never be delegated blindly. Key Takeaways An agentic software factory coordinates AI agents across the complete software delivery lifecycle. Faster AI-assisted coding can create review bottlenecks unless downstream stages also improve. Humans retain ownership by defining guardrails, specifications, approvals, and security requirements. Workflow orchestration connects context, automation, observability, incident handling, and feedback loops. What Is a Software Factory? A software factory is not just a collection of developer tools. It is an operating model where software delivery is designed as a smooth, observable workflow from idea to production and back into improvement. In a car factory, a vehicle moves through assembly, painting, quality inspection, final assembly, and delivery. People are involved at important checkpoints, but the process does not restart from scratch at every station. It is structured, repeatable, and connected. The same model works for software. In an agentic software factory, the flow can look like this: Requirements and desi

A few months ago we shipped the epilot CLI , and it quietly became one of my favorite tools. One command, npx epilot , gives you every single epilot API operation in your terminal: entities, journeys, workflows, pricing, files, permissions, 50+ APIs. Interactive pickers if you're exploring, --json and --no-interactive if you're scripting. It also turned out to be a perfect match for AI agents like Claude. Agents are great at driving CLIs: they discover operations, read the help, make calls, parse the JSON. No custom integration or MCP server needed, the CLI is the integration. And because handing an agent live CRM access is a scary idea, the CLI ships with two safety nets, both enforced server-side: # A session that physically cannot write. The restriction is baked # into the token, so the bearer can't turn it off. epilot auth login --readonly # A token that additionally gets all PII anonymized in every response epilot access-token createAccessToken -d '{ "name": "AI agent token", "read_only": true, "anonymize": true }' Read-only plus anonymized means an agent can explore, analyze and report on your real org all day, and the worst it can do is read data it can't even de-anonymize. Now we've made the CLI even better. On top of the raw API commands, we added app facades : a set of high-level epilot app commands that take you from an empty folder to a working app installed in your org. And that's what this post is really about, because apps are where the fun is. What are epilot Apps? epilot is very configurable out of the box: journeys, workflows, automations, pricing. But at some point every team hits a wall, something the UI simply doesn't offer. A custom tab on the contact or opportunity page showing data from your own systems A whole custom page in the epilot navigation Your own block in the journey builder A widget in the end-customer portal A flow action that calls your API when a workflow step runs An external product catalog or an API proxy to your backend That

If you've ever stared at a 15,000-file monorepo wondering "what actually breaks if I touch this...

Developers are owning more of the delivery system around code, not just code itself. Join us during GitHub Universe to meet other devs, learn something new, and explore what's next. The post From coder to orchestrator: How agents shift the role of a developer appeared first on The GitHub Blog .

There are two ways to work with an AI agent and I had tried both. Write the thing yourself and hand over only the tedious parts. Or hand over the whole task and audit whatever comes back at the end. The first is slow. The second is fast right up until it is wrong, and by then the wrong thing is finished. I expected this series to be about forcing a third option into existence. Nine parts of making an agent follow a workflow it would rather skip. That is not what happened. I never had to enforce it once. The queue that started this had a payload contract nobody had verified, and each phase after that cost me something before it gave anything back. A plan that would not move until the risk register named the provider contract the brief had only guessed at. A build that missed nothing except what my own brief left out. A review that stopped handing back a feeling and started handing back a verdict on every requirement I had already called done. A matrix instead of a trusted green run. A rollback with a name on it before anything got called shipped. And a retrospective that would not let a lesson through until it had checked itself against the trail. Eight parts of that. What I did not expect was which part turned out to be automatic. The Fight I Expected Never Started By the time I finish writing a requirement, I already know roughly what it is going to cost. Most engineers do. You can feel the difference between a one-line fix and something that is going to touch four files and a migration before you have written a single line of it. What I assumed was that the agent could not feel that, and that policing the gap would be my job forever. Reminding it to run the chain. Catching it when it decided a spike was small enough to skip. It has not needed the reminder. Small bugs do not trigger a brief and a plan, and they should not. A standard requirement, a spike, anything long or cross-cutting, runs the full cycle in order. The classification lands where I would have put i

I used to think financial apps were designed mostly for people who already understood finance. You open an app, see dozens of charts, numbers, and settings, and sometimes your first thought is: “Okay… where do I even start?” AI is slowly changing that experience. The most useful AI applications in fintech are not always about making predictions. In many cases, they are about removing friction: organizing information, automating repetitive tasks, and helping users understand complicated processes. We can already see this trend in different areas. Companies like Stripe are using technology to simplify online payments, while platforms like Plaid help connect financial data between different services. The same idea is appearing in other parts of finance too. Some digital financial platforms are exploring automation tools that help users create more structured workflows instead of manually managing every step. For example, platforms like BYDFi have introduced automated tools that allow users to use predefined strategies rather than constantly monitoring every market movement. Of course, automation does not replace human decisions. A smarter tool does not mean a person can ignore research or risk management. For developers, the interesting challenge is not building the most complicated system. It is building something that makes complicated things easier for real users. Maybe the future of fintech is not about adding more buttons. Maybe it is about helping people need fewer buttons.

The Quest Begins (The "Why") I was staring at a pull request that felt like a boss level in a retro arcade game—except there were no extra lives. The code was a massive if/else if/else chain that decided how to handle different JSON payloads coming from a third‑party API. Each branch did almost the same thing: validate a few fields, map them to our internal model, then call a service. The only thing that changed was the shape of the incoming object. Every time a new endpoint was added, a developer had to copy‑paste the whole block, tweak a few field names, and pray they didn’t miss a comma. Reviewing it felt like watching someone try to solve a Rubik’s cube by rotating random faces—you could get lucky, but most of the time you just made a bigger mess. I kept asking myself: Why are we writing the same logic over and over? The answer was hiding in plain sight: we weren’t seeing the pattern. The Revelation (The Insight) The breakthrough hit me while I was refactoring a tiny utility that turned a list of user IDs into a set. I realized I wasn’t writing a new algorithm each time—I was applying the same shape of solution: take an input, transform it, then feed it to a consistent consumer . In other words, the problem wasn’t “how do I handle payload X?” It was “how do I dispatch the right transformation based on a key?” That’s a classic dispatch table (or strategy pattern) problem. The “aha!” moment was when I looked at the chain and saw that each branch could be expressed as a function: function handleOrder ( payload ) { /* … */ } function handleRefund ( payload ) { /* … */ } function handleShipment ( payload ) { /* … */ } All of them shared the same signature: (payload) => Result . If I could map a discriminator (like payload.type ) to the correct function, the whole if/else monster would collapse into a single lookup. That’s the pattern top coders spot instantly: repetitive conditional logic → a table of behaviors . Once you see it, the code writes itself. Wielding the

Instacart introduced Blueberry, an AI-assisted incident response system that helps on-call engineers investigate production issues faster. It combines AI agents, operational data, and historical incident knowledge to generate grounded root cause hypotheses in Slack. It uses parallel subagents, MCP integrations, and incident history to reduce investigation time while keeping engineers in control. By Leela Kumili