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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Your backend framework will outlive your current team. Choose one that the next team can still navigate — here's why .NET has been that framework for Netflix, GitHub, and Stack Overflow for over two decades. Summary Twenty-three years. That's how long .NET has been running in production. Most frameworks from that era got abandoned, forked beyond recognition, or replaced entirely — .NET kept showing up. Netflix still uses it. GitHub uses it. Stack Overflow, which has probably saved more developer careers than any single resource on the internet, runs on ASP.NET. None of these teams are using it out of inertia. They're using it because it works under conditions that expose every weakness in a poorly designed system. This article gets into how .NET actually works, what it gives teams day-to-day, and whether it makes sense for what you're building now. Key Takeaways: One codebase, five platforms — Windows, macOS, Linux, Android, iOS. No rewrites, no platform-specific forks. Three languages, one project — C#, F#, and Visual Basic coexist without forcing a rewrite. The performance tooling ships with it — JIT compiler, AOT compiler, CLR memory management, Garbage Collector. All out of the box. Why Is .NET Still Around? Honestly, this question is worth sitting with for a second — because in software, most things don't survive twenty years. They solve the problem of the moment, get widely adopted before anyone finds the sharp edges, and then get quietly replaced when something newer comes along and the migration pain seems worth it. .NET didn't go that way. Some of that is Microsoft backing — resources, long-term support commitments, a developer community that doesn't dissolve when priorities shift. But backing alone doesn't explain it. Plenty of well-resourced frameworks have died. What actually kept .NET alive is that the foundational architecture held up. The cross-platform capability wasn't duct-taped on in 2020 because everyone suddenly cared about Linux. It was in the

Because "we'll be in touch" is not an observable state. I submitted a job application recently. It...

submitted by /u/rahmanism [link] [留言]

This isn't my main piece for the week, it's more of a "contributes nothing to knowledge" kind of post. Last week I took another look at my portfolio and thought, "Hey, why not make this feel a bit more like me?" So I set out to give it a makeover, stuffed as much of my personality into it as I could, and et voilà, done. The old one was kinda too formal. TL;DR: I gave my portfolio a personality transplant. If you'd rather just look than read: a-thedeveloper.vercel.app Vibe / Tone Option By default, the professional option is enabled. But if you're not too sensitive and want to have a little fun, try toggling over to the unfiltered version of me, lol. I don't actually talk like that in real life anymore, but having grown up speaking English, that's pretty much how I sounded back in my teenage years. I was a grumpy teenager like everyone else, the difference is I was extra grumpy compared to most. 😭 I also lost access to my Instagram account, so all of it is still sitting there, public, for anyone to see. Every day I hope that account just quietly gets deleted. And if you're wondering whether that same energy has been erased, nope, it's still very much here. I just keep it contained to appropriate contexts now, lol. I also found these while digging through my old microsoft drive, weird 16 year old me stuff. I actually said this in a debate, by the way. Can't remember if my team won that one or lost. Weather Options Kinda irrelevant to how it actually describes my portfolio, but I initially wanted to make rainy the only option, because I'm a big fan of dark, gloomy, cloudy weather — the kind that makes England look like heaven to me. 😭 Then I thought, why not just have all of them? So now each weather option comes with its own falling elements based on the selection, plus music that I feel fits the atmosphere. Again, it doesn't really serve any practical purpose, but I think it's a nice little touch to have, haha. DEV Writing Views with an API Key When I joined DEV in 2

submitted by /u/cdb_11 [link] [留言]

As of August 26, 2026, GitHub reports that 398 merged pull requests carry my name on the author line. Twenty nine of those live inside repositories that I own myself, so I removed them from this count on purpose. What remains is the number that actually matters to me: 369 pull requests merged into other people's repositories , across 33 external projects , maintained by strangers who had zero reason to trust my code. Every single one of those 369 merges is real, dated, and linked in this post. Nothing here is rounded up and nothing is claimed without proof. If you want to skip my writing entirely, open the search query in section two and run it yourself. That is the whole point of this article. You should never have to take a stranger's word about their own stats. Jump To Any Section How I Verified These Numbers | The Complete Scoreboard | 2024 The Year Of Volume | 2025 Fewer Pull Requests Higher Quality | 2026 The Year Production Code Got Merged | What 369 Merges Taught Me | Frequently Asked Questions | Where To Find Me How I Verified These Numbers I did not count these by hand. I queried the GitHub search API directly, which means the numbers come from GitHub itself, not from my memory or my ego. You can reproduce everything in this post with one click: https://github.com/search?q=is%3Apr+is%3Amerged+author%3Aaniruddhaadak80&type=pullrequests Or if you have the GitHub CLI installed: gh api -X GET search/issues -f q = "is:pr is:merged author:aniruddhaadak80" That query currently returns 398 results. I then filtered out every repository under my own account, which left exactly 369 external merges. The math is boring on purpose: 398 minus 29 own repository merges equals 369. One more honest number before we go further. I have submitted 918 pull requests in my lifetime so far. That means fewer than half of everything I ever sent got merged. Rejection is not the exception in open source, it is the price of admission, and anyone who shows you a 100 percent merge rate is

The strongest candidate I ever voted no on solved the problem in eleven minutes. Clean. Optimal. Caught the edge case I normally have to hint at twice. Then I opened my notes to write the scorecard and found one line: "Solved it. I have no idea how." That is the short version of why strong engineers fail coding interviews. Not because they can't code. Because nothing they did survived the trip from the room to the scorecard. The interview is not the thing being graded. The document I write forty minutes later is the thing being graded, and you are not in the room when it gets read. TL;DR Strong engineers fail coding interviews mostly on signal density , not correctness. A silent correct answer scores lower than a narrated near-miss. Interviewers score 3-4 rubric axes (problem solving, coding, communication, and for senior roles, judgment) and each axis needs quotable evidence , not vibes. The decision happens in the debrief , where ambiguity defaults to no. "Lean hire" across the board is a rejection at most companies. The most common senior failure is solving a senior problem like a junior : no scoping, no tradeoffs, no failure modes, no tests. Fix it by talking in sentences your interviewer can transcribe verbatim: assumption, tradeoff, complexity, test. What do interviewers actually score in a coding interview? Not "did you get the answer." Almost every structured loop I've been part of scores a fixed rubric, and correctness is one box inside one axis. Here is roughly what the form looks like: Axis What it's really asking What lands on the scorecard Problem solving Did you scope before you built? "Asked whether input fits in memory before choosing an approach." Coding Would this survive code review? "Named things well, extracted a helper, no off-by-one." Communication Could I follow you in real time? "Told me the plan first, then coded the plan." Judgment (senior+) Do you know what breaks in prod? "Unprompted, called out the retry storm risk." Notice what every r

Customizing cell backgrounds in Excel is one of the fastest ways to transform a plain data dump into a professional, scannable report. Whether you need to highlight headers, flag key metrics, or add visual polish to dashboards, the Free Spire.XLS for .NET library makes it easy to apply solid fills , texture patterns , and gradient effects programmatically. In this guide, you'll learn how to implement each style with concise C# examples. Prerequisites Install the library via NuGet Package Manager: Install-Package FreeSpire.XLS Then add the required namespaces to your project: using Spire.Xls ; using System.Drawing ; 1. Solid Fill (Flat Background) The solid fill is the most common background type—ideal for headers, totals, or status-based highlighting. using ( Workbook workbook = new Workbook ()) { Worksheet sheet = workbook . Worksheets [ 0 ]; CellRange cell = sheet . Range [ "B2" ]; cell . Text = "Solid Background" ; // Solid fill requires the pattern to be explicitly set to Solid cell . Style . FillPattern = ExcelPatternType . Solid ; cell . Style . Color = Color . LightGreen ; workbook . SaveToFile ( "CellSolidColor.xlsx" , ExcelVersion . Version2016 ); } ⚠️ Crucial : Always set FillPattern to ExcelPatternType.Solid before assigning a color. If omitted, the color change will be ignored. 2. Texture Fill (Pattern Overlay) Texture fills overlay a repeating pattern (e.g., brick, checker, or angle) over a base color. They're perfect for subtly distinguishing data categories without overwhelming the reader. using ( Workbook workbook = new Workbook ()) { Worksheet sheet = workbook . Worksheets [ 0 ]; CellRange cell = sheet . Range [ "B2" ]; cell . Text = "Texture Background" ; // Angle texture pattern cell . Style . FillPattern = ExcelPatternType . Angle ; cell . Style . Color = Color . LightGray ; // Base background color cell . Style . PatternColor = Color . Beige ; // Pattern overlay color workbook . SaveToFile ( "CellPattern.xlsx" , ExcelVersion . Version2016 ); } N

I built Unicode Text Tools , a free site with a bunch of text converters — superscript, subscript, bubble/circled text, upside-down text, small caps, and more. Type something, get it transformed, copy it out. The whole engine is one dependency-free JS file built entirely from character mapping tables . No AI, no server, no libraries. Here's why that's the right architecture for this class of tool, and how the trickier conversions work. The core idea: it's all just lookup tables Every conversion on the site is a function that maps each input character to a Unicode character (or does a small transform). The simplest cases are pure dictionaries: // Superscript (full a-z, 0-9) var SUP = { a : ' ᵃ ' , b : ' ᵇ ' , c : ' ᶜ ' , d : ' ᵈ ' , e : ' ᵉ ' , f : ' ᶠ ' , g : ' ᵍ ' , h : ' ʰ ' , i : ' ⁱ ' , j : ' ʲ ' , k : ' ᵏ ' , l : ' ˡ ' , m : ' ᵐ ' , n : ' ⁿ ' , o : ' ᵒ ' , p : ' ᵖ ' , q : ' ᵠ ' , r : ' ʳ ' , s : ' ˢ ' , t : ' ᵗ ' , u : ' ᵘ ' , v : ' ᵛ ' , w : ' ʷ ' , x : ' ˣ ' , y : ' ʸ ' , z : ' ᶻ ' , ' 0 ' : ' ⁰ ' , ' 1 ' : ' ¹ ' , ' 2 ' : ' ² ' , ' 3 ' : ' ³ ' , ' 4 ' : ' ⁴ ' , ' 5 ' : ' ⁵ ' , ' 6 ' : ' ⁶ ' , ' 7 ' : ' ⁷ ' , ' 8 ' : ' ⁸ ' , ' 9 ' : ' ⁹ ' , ' + ' : ' ⁺ ' , ' - ' : ' ⁻ ' , ' = ' : ' ⁼ ' , ' ( ' : ' ⁽ ' , ' ) ' : ' ⁾ ' }; The transform itself is trivial — walk the string, look up each char, append the mapped value (or the original char if unmapped). The work is in the tables: knowing which Unicode blocks exist, what's 1:1 reversible, and what's incomplete. The Unicode reality check Here's the thing nobody tells you about Unicode text transformation: the blocks are inconsistent. Superscript : complete for a-z and 0-9 — fully reversible. Subscript : incomplete — there's no subscript b , c , d , f , g , q , w , y , z . If you map an input with those letters, you have to decide what to do with them. Small caps : x has no small-cap form ( ꞯ is the closest, but it's a different character and looks wrong). j is a problem too — the Unicode small-cap ᴊ collides visually

A polished UI can make a product feel completely different, but getting there usually takes more than...

The same zsh script could list ~/Documents when I ran it in Terminal. Started as a LaunchAgent, it failed with: ls: /Users/administrator/Documents: Operation not permitted The LaunchAgent had the same user ID, the same $HOME , and the same script. That combination makes this look like a Unix permission problem. In this test it was not. The useful discriminator was the launch context: access succeeded from Terminal, failed from launchd , and still succeeded for a path outside the protected folder. I reproduced this on macOS 15.6.1 (Darwin 24.6.0) with a LaunchAgent in gui/501 . The probe was removed after the test. Why chmod is the wrong first check The obvious suspects were file ownership, a wrong home directory, or a job running as another user. The probe printed those facts before touching the files: #!/bin/zsh print -- "user= $( id -un ) uid= $( id -u ) " print -- "home= $HOME pwd= $PWD " /bin/ls " $HOME /Documents" 2>&1 | /usr/bin/head -5 /bin/cat " $HOME /Documents/vinh/working/CLAUDE.md" 2>&1 | /usr/bin/head -1 # Negative control: outside Documents /bin/ls " $HOME /.pf004" 2>&1 | /usr/bin/head -5 The two runs produced this difference: Check Terminal LaunchAgent in gui/501 User / uid administrator / 501 administrator / 501 $HOME /Users/administrator /Users/administrator ls ~/Documents Listed entries Operation not permitted cat inside ~/Documents Read the file Operation not permitted ls ~/.pf004 Listed entries Listed entries The working directory differed, but the script used absolute paths under $HOME , so PWD=/ did not explain the denial. The negative control mattered more: the LaunchAgent could read another directory owned by the same user. Changing ownership or mode bits would not explain why only the launch context changed the result. The owning layer is the privacy context On this machine, the access decision was attached to how the process was launched, not just to uid 501. Terminal had a privacy context that allowed access to the user's Documents folder.

Do you know Git? Of course you do! Today, I’ve got a few of my favorite Git commands for...

GitHub Copilot premium requests are the metered unit that determined how much advanced Copilot usage your plan covered, and if you are searching for how they work in mid-2026, you need two answers, not one. First, the mechanics: a premium request is consumed each time you use an advanced Copilot feature, scaled by a per-model multiplier, against a fixed monthly allowance that came with your plan. Second, the news: as of June 1, 2026, GitHub moved Copilot from request-based billing to usage-based billing , and premium requests are now officially labeled "legacy" throughout GitHub's own documentation. Their replacement is GitHub AI Credits, metered at one cent per credit. Both systems matter today. Annual Copilot Pro and Pro+ subscribers who stayed on their existing plans are still billed in premium requests, and every question about the new credits model (allowances, overages, admin controls) is easier to answer if you understand the system it replaced. Here is the complete picture, with the numbers. What is a premium request? GitHub's definition is simple: a request is any interaction where you ask Copilot to do something, whether that is generating code, answering a question, or reviewing a pull request. Routine interactions, like inline code completions, are unlimited on every paid plan and never touch the meter. Premium requests are the interactions that use more advanced processing, and they draw down a monthly allowance: Copilot Chat : one premium request per user prompt, multiplied by the model's rate (ask, edit, agent, and plan modes all count). Copilot code review : each review consumed one request originally; since June 1, 2026 it carries a 13x multiplier , so a single review deducts 13 premium requests. Copilot coding agent and CLI : one premium request per prompt or session, times the model's rate. Only your prompts count; the autonomous tool calls Copilot makes along the way do not. Spark : a fixed rate of four premium requests per prompt. The critical n

Following the discussion on named AI personas and trust — here's the engineering side: how do you keep AI-status disclosure genuinely persistent throughout a conversation without making the interface feel robotic or constantly interrupting the experience a named persona is meant to create? The Naive Approaches Both Fail Option A: One disclaimer, message one, never again. Trivially easy to implement, but gets forgotten within a few exchanges — exactly the failure mode worth avoiding for personas carrying real emotional weight. Option B: Repeat "I am an AI" every single message. Technically persistent, but breaks the actual UX a named persona is trying to create, and users will tune it out as noise within a few messages anyway — repetition without variation loses its signal value fast. Neither is a good engineering solution. The better pattern is contextual, adaptive disclosure. Pattern: Risk-Weighted Disclosure Frequency python class DisclosureManager: def init (self, base_interval=8, high_risk_interval=3): self.base_interval = base_interval self.high_risk_interval = high_risk_interval self.messages_since_disclosure = 0 def should_inject_disclosure(self, message_risk_level: str) -> bool: interval = ( self.high_risk_interval if message_risk_level == "high" else self.base_interval ) self.messages_since_disclosure += 1 if self.messages_since_disclosure >= interval: self.messages_since_disclosure = 0 return True return False message_risk_level comes from the same classification pass used for scope/escalation detection covered in earlier persona-guardrail architecture — emotionally sensitive or high-stakes exchanges trigger disclosure more frequently than routine ones. Pattern: Disclosure Woven Into Persona Voice, Not Bolted On Rather than an interrupting system message, integrate the reminder into the persona's actual response style: python def inject_natural_disclosure(response_text, persona_config): disclosure_phrases = persona_config.disclosure_variants # e.g. for "Ок

I want to be upfront about something. I didn't figure this out proactively. I figured it out after my second burnout in three years — sitting in a period of forced recovery, unable to look at a code editor without feeling a specific kind of dread that I couldn't logic my way out of. I'd done everything the burnout recovery advice said to do. Took time off. Set better boundaries at the new job. Worked on the psychological stuff. All of it helped. None of it explained why recovery felt so much harder and slower than it should. Then I got bloodwork done. And the picture became considerably less mysterious. The Diagnostic Output bash $ bloodwork --full-micronutrient-panel --date=recovery-period [CRITICAL] vitamin-d: 18 ng/mL target: 40-60 ng/mL status: severely deficient duration: estimated 2+ years note: dopamine synthesis impaired at this level [CRITICAL] rbc-magnesium: low note: serum looked normal — wrong metric duration: unknown — never previously tested correctly note: HPA axis running unregulated [HIGH] omega3-index: 3.1% target: 8%+ status: neuroinflammation elevated note: western diet + zero supplementation [HIGH] hs-crp: 2.9 mg/L target: <1.0 mg/L status: significant systemic inflammation note: never measured, thoroughly normalized [WARNING] ferritin: low-normal note: passing standard panel, causing fatigue bugs-found: 5 bugs-known: 0 recovery-speed: severely impaired by all of the above Two burnouts. Same underlying biology. Neither time did anyone suggest checking any of these markers. What the Numbers Actually Meant Vitamin D at 18 ng/mL: Vitamin D is a direct input to dopamine synthesis. The enzyme that produces dopamine requires it. I had been trying to rebuild motivation and find meaning in work — the core challenge of burnout recovery — while running a dopamine system without adequate substrate. javascript // what I was trying to do dopamine.rebuild() // what the system had to work with vitaminD: 18 // severely deficient tyrosineHydroxylase.efficiency:

submitted by /u/wineandcode [link] [留言]

Hi all, Im an integration engineer with a heavy focus on software to hardware integration. Recently I have been doing less and less coding at work so I wanted to practice my skills, this lead me to creating my first web based app Daily Redzone Im looking for people to beta test and offer any and all feedback. I haven’t been able to stress test it enough by myself so im looking to have people beat and abuse it to lmk how it functions It is a daily football game where each day you face a new redzone situation and you have 4 downs to score. There is also a head to head mode where you can play friends. Lmk what you all think! Thank you. submitted by /u/Fruity_Stones [link] [留言]

submitted by /u/mttd [link] [留言]

Instagram says the process can produce a first pass in under 10 seconds, potentially saving creators significant editing time while making video creation more approachable for people who don't have much experience with editing software.