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Write for people (vickiboykis.com)
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Roc’s first numbered release, 0.1.0, is on the horizon. This talk previews what we’re aiming to include, the key language and tooling milestones needed to get there, and what the release will mean for people interested in trying, using, or contributing to Roc.

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bots- is there a solution (programming.dev)

bots on websites, on apps on forums, has anyone found a real solution to getting rid of bots? and bot accounts? if so id love to know any solutions

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The adoption of AI Tooling ironically encourages better practices. Now you have to actually do everything you've been putting off.

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submitted 2 weeks ago* (last edited 1 week ago) by TreesInSpace@sh.itjust.works to c/programming@programming.dev

Sorry If this is the wrong place, just delete it for me, downvotes hurt my feelings.

Did you guys know the famous flipqlo actually required you to be always online? I didn't! Until my internet was gone for 100 days thanks to my Ips. Heck I made my own version. Requires nothing. It even works on windowsXP x86! Multi-lingual and shows date too! I will add virustotal soon, It is packed with upx. ~~Size 350kb~~ now 900kb without compressions because vt hates it!! Download: https://raw.githubusercontent.com/InevitableDays/InevitableDays.github.io/refs/heads/main/Others/Demos/X99Clock.scr Vt 3/71 you can test it too. I will add link I am on mobile as of now

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submitted 2 weeks ago* (last edited 2 weeks ago) by rimu@piefed.social to c/programming@programming.dev

studies show a clear trend – output is up (more code, more commits, bigger diffs), but outcomes don’t reflect that trend. If anything, the average team is taking longer to ship worse software

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Periphery, a famous and efficient tool to check dead code, moves from open source model to commercial model 😭

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Mojo 1.0 is here (www.modular.com)
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Making holograms with a pen plotter (blog.jordan.matelsky.com)
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I am thinking of doing freelance web development and I'm currently building my portfolio for it. However, I don't have any degree at all and I live in an expensive country. My friend however says that I still might have a chance if I make good quality stuff. Right now I'm even working on creating a video platform website similar to YouTube (with it being fully functional) just to impress potential customers.

Would that work?

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Realistically, how much more does AI for coding have to get out of leaning and training more for formal verification (with eg Lean, Rocq etc and their interop with mainstream langs)

If AI hasn’t really pursued this strongly, then maybe a good amount, right?

I can imagine a scenario where you no longer review the code but a simulation of all the behaviours of the system, perhaps before any code has been written, and then you can trust the output will have accordingly …?

What practical limits of formal verification are there for this?

And for the anti AI folks, I’m anti AI too and I don’t use it or want to use it at all. I’m thinking about the future here.

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submitted 2 weeks ago* (last edited 2 weeks ago) by supersquirrel@lemmy.ca to c/programming@programming.dev

cross-posted from: https://lemmy.ca/post/69219331

Among other things github interaction with projects is analyzed so I think this is relevant to programming too.

open access paper https://arxiv.org/abs/2511.03877

Cross-channel prediction outperforms same-channel pre- diction for early input-horizon, across all models. This is consistent with correlations plots in Figure 3 and Figure 2....

...

We establish Lead-Lag Forecasting (LLF) as a formal prediction problem, motivated by the gap between observed lead-lag dynam- ics in important domains—including scientific and technological impact—and popular time series forecasting benchmarks. We cat- alyze research on LLF by curating and releasing two novel datasets: arXiv papers and GitHub repositories. We establish lead-lag rela- tionships in streams of activity data and provide baseline numbers for several standard supervised machine learning methods on the task of predicting a 5-year outcome from as little as one month of observation. While our results demonstrate the existence of predic- tive signal, we speculate that there are opportunities for innovation to improve predictions.

Smells like Category Theory to me!

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Been running a handful of scheduled tasks on two VPS instances for about a year now — nightly DB dumps, cache warmers, a couple of data sync scripts. Had everything piped to log files and thought I was covered.

Then last month one of the sync scripts started hanging mid-execution. It never crashed, never threw an error, just... sat there. The log showed the start timestamp but no finish line. Took me four days to notice because I wasn't checking logs daily (who does).

After that I looked into what people use to catch this kind of thing.

What I tried:

Healthchecks.io — solid, does the job. You curl a URL at the end of your cron and if the ping doesn't arrive within the expected window, you get an alert. Simple concept.

I also tested WatchCron which works on the same principle but felt a bit snappier to set up for multiple jobs. Has a dashboard that shows timing patterns across runs, which helped me spot that one of my backup jobs was gradually taking longer each week — would've missed that with just pass/fail alerts.

The pattern that works for me now:

#!/bin/bash
# at the end of each cron script
curl -fsS -m 10 --retry 3 $MONITOR_URL > /dev/null

If the script hangs or exits early, the ping never fires, and I get a Slack notification within minutes.

One thing I changed — I stopped redirecting cron output to /dev/null. Feels obvious in hindsight but I see it everywhere in tutorials. If your task does fail, you want that output in the mail spool or a log, not gone.

What's your setup for catching silent failures? Curious if anyone's doing something beyond the "ping on success" model — like tracking execution duration or exit codes.

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submitted 2 weeks ago* (last edited 2 weeks ago) by onlinepersona@programming.dev to c/programming@programming.dev

It's that time again and I'd like to make my life easier. I have multiple CVs for different positions and in different languages. Recruiters and companies want the CVs in PDF.

My current workflow is to sequentially edit the .odts (wording then layout because things move around depending on length), maybe the style, then save and export to PDF. That can take a while, so here I am, wondering if it can be sped up by simply editing a text file and generating CVs with certain themes.

Or what your workflow is. Maybe this isn't worth automating...

Solution

Finally went with RenderCV (thank you @bert@sh.itjust.works), which also runs locally. YAML in, PDF, PNG, HTML, etc. out. It comes with 10 themes or so. Haven't looked at what it take to create my own (I'm lazy).

Contender was JSON Resume, but the site is down and I tried it years ago but never got it to work properly.

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Back in January, I received a note from a senior software engineer in Silicon Valley. He described himself as an AI skeptic who became converted after trying Claude Code for the first time. “Overnight, it changed the way I do my job,” he wrote. “It’s really, really good.”

As he explained, he no longer used a standard development environment. Instead, he “exclusively uses Claude Code” to get the job done, interacting with the tool in a terminal window and allowing it to program on his behalf.

“If I had to guess,” he concluded, “I’d say a task that would have taken me a week now takes me 2 days.”

This past winter, when I surveyed more than 300 software developers to learn how AI was transforming their jobs, the majority told a similar tale of shifting from writing their own code to instructing AI agents. The speed with which this new tool became ubiquitous in this industry was stunning.

This story matters for the rest of us because AI coding tools have emerged as the prime example of the power of AI—the first step of many more soon to come on this technology’s disruptive march through our work and our lives.

But what if the reality here is more complicated?

Last week, I received a new message from that same senior engineer who wanted to share an alarming addendum to his tale…

“I’m writing to give you an update on my current thinking about the state of AI in software engineering,” he began, “because my attitude has shifted quite a bit.”

He told me that features he generated using Claude Code ended up crashing their product on two different occasions. His boss told him that if it happened one more time, he’d be fired. “I’ve never had quality issues like this before in my career.”

The problem is that code produced by an AI agent looks reasonable, but can contain ‘hard-to-spot bugs’ that end up causing major problems. As a result, you should carefully review your agent’s output, but this is difficult. As the engineer told me, it’s “famously hard” to understand code you didn’t write yourself, so this extra step becomes “easy to just blow it off (especially when we are all trying to ‘10x’ our velocity).” Soon, systems start to break.

“The coding harnesses are useful and make life as a developer easier,” he summarized, “but they also encourage laziness.”

In response to these issues, this disillusioned engineer has returned to largely programming by hand. Here’s how he explained his current philosophy:

“Writing your own code, slowly but surely, and using LLMs for narrow or particularly annoying tasks (say like writing tests or throw-away scripts), is the best way to produce the highest quality code, since it’s the only way to properly understand it.”

Here’s the thing: he’s not alone.

I increasingly hear similar rumbles from many other people in the software industry (see, for example, ​this podcast episode​ from May). Tools like Claude Code can feel like magic, but the strategy of outsourcing all code production to AI isn’t currently sustainable.

In addition to reliability issues, it often engenders a mind-numbing workflow and an environment where junior developers will never acquire the expertise to become senior developers capable of designing complex systems.

Meanwhile, as the frontier labs reduce their subsidies on underlying computing costs, the old habit of burning through as many tokens as possible in search of workable results is proving prohibitively expensive.

From the outside, software development seemed like the poster child for AI’s potential. On the inside, it’s a mess.

This doesn’t mean that coders will abandon AI; its facility with programming languages is too valuable to ignore. But I think there’s a lot more work to be done trying to figure out how to integrate AI into this industry in a way that actually works.

This is a key point.

This last year has been exhausting. The PR departments of the frontier labs have done an excellent job convincing us that AI developments are occurring at an astounding, world-changing rate. But if you zoom out, it becomes clear that almost every “breakthrough” since last summer has concerned the narrow domains of computer code and math, which are defined by highly structured languages and come accompanied by massive amounts of specialized training data.

And yet, even in this best-case-scenario setting for AI, we’re still struggling to figure out how to actually use these tools in a way that makes sense in the long run.

This doesn’t mean that AI doesn’t work or is useless. But it does emphasize an important truth: AI is not a magic “infinity machine” that can solve all our problems, and ultimately deliver us a sense of meaning in a cold, confusing world. It’s a normal technology, and perhaps it’s time we start talking about it that way.

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