Programming With Less Than Nothing
Lambda calculus is for wimps. Real programmers use combinatory logic.
Code
Che cos'è il Basilisco di Roko, l'oscura teoria che ha fatto incontrare Elon Musk e Grimes
Elon Musk ha trasformato un vecchio esperimento mentale su una super-intelligenza artificiale killer in una pickup line.
L’esperimento mentale è stato inizialmente pubblicato da Less Wrong — un forum e blog su razionalità, psicologia e intelligenza artificiale in generale — nel luglio 2010 da un utente chiamato Roko. Al livello più base del tutto, l’esperimento mentale parla delle condizioni in cui sarebbe razionale per una futura super-intelligenza artificiale uccidere gli umani che non hanno contribuito alla sua creazione.
se una super-intelligenza decide le sue azioni per fare sì che siano quanto più positive possibili per il benessere degli esseri umani, allora non si fermerà mai perché le cose possono sempre andare un po’ meglio.
Chiunque non stia lavorando per creare quella macchina si sta frapponendo al progresso e dovrebbe essere eliminato per riuscire a raggiungere più rapidamente quell’obiettivo.
ora che lo avete letto, siete tecnicamente coinvolti in esso. Non avete più una scusa per non contribuire alla creazione di questa super-intelligenza artificiale e se scegliete di non farlo, sarete uno dei primi obiettivi dell’intelligenza artificiale.
Quando Roko ha pubblicato la sua teoria del basilisco su Less Wrong, Yudkowsky si è piuttosto spazientito, a tal punto da cancellare il post e vietare ogni tipo di discussione sul basilisco dal forum per cinque anni. Quando Yudkowsky ha spiegato le sue azioni sulla subreddit Futurology qualche anno dopo il post originale, ha affermato che l’esperimento mentale di Roko dava per scontato di riuscire a superare una serie di ostacoli tecnici nella teoria delle decisioni, e anche se ciò fosse possibile, Roko stava efficacemente diffondendo delle idee pericolose, proprio come i cattivi della storia di Langford.
IT pop
Largest study of its kind shows AI assistants misrepresent news content 45% of the time – regardless of language or territory
An intensive international study was coordinated by the European Broadcasting Union (EBU) and led by the BBC
31% of responses showed serious sourcing problems – missing, misleading, or incorrect attributions.
20% contained major accuracy issues, including hallucinated details and outdated information.
The Internet's Biggest Annoyance:Why Cookie Laws Should Target Browsers, Not Websites | NEDNEX
Save and Share: Click. Ugh. Another one. You know the drill. You land on a new website, eager to read an article or check a product price, and before the page even finishes loading, it appears: the dreaded cookie banner. A pop-up, a slide-in, a full-screen overlay demanding you “Accept All,” “Manage Preferences,” or navigate… Continue reading The Internet’s Biggest Annoyance:Why Cookie Laws Should Target Browsers, Not Websites
Imagine if every time you got into your car, you had to manually approve the engine's use of oil, the tires' use of air, and the radio's use of electricity. It’s absurd, right? You’d set your preferences once, and the car would just work.
It Doesn't Actually Give Us Control: The illusion of choice is not choice. When the options are "Accept All" or "Spend Five Minutes in a Menu of Legalese," the system is designed to push you toward the path of least resistance.
Today is when Amazon brain drain finally caught up with AWS • The Register
column: When your best engineers log off for good, don’t be surprised when the cloud forgets how DNS works
COLUMN "It's always DNS" is a long-standing sysadmin saw, and with good reason: a disproportionate number of outages are at their heart DNS issues. And so today, as AWS is still repairing its downed cloud as this article goes to press, it becomes clear that the culprit is once again DNS. But if you or I know this, AWS certainly does.
Tools
Game over. AGI is not imminent, and LLMs are not the royal road to getting there.
First slowly, and then all at once, dreams of LLMs bringing us to the cusp of AGI have fallen apart.
• June, 2025: the Apple reasoning paper confirmed that even with “reasoning”, LLMs still can’t solve distribution shift, the core Achille’s heel in neural networks that I have been writing about for nearly 30 years.
SQL Anti-Patterns You Should Avoid - by Jordan Goodman
Introduction
One of the most common mistakes I’ve seen is developers using SELECT DISTINCT as a quick way to eliminate duplicates that appear after a bad join. It’s an easy fix, but it hides a deeper problem. Usually, the duplicates exist because the join condition is incomplete or the relationship between tables isn’t truly one-to-one
How does ChatGPT ‘think’? Psychology and neuroscience crack open AI large language models
Researchers are striving to reverse-engineer artificial intelligence and scan the ‘brains’ of LLMs to see what they are doing, how and why.
But with conventional software, someone with inside knowledge can usually deduce what’s going on,
worked for a dozen years — will have a good idea why. “Here’s what really terrifies me” about the current breed of artificial intelligence (AI), he says: “there is no such understanding”, even among the people building it.
Martin Wattenberg, a computer scientist at Harvard University in Cambridge, Massachusetts, says that understanding the behaviour of LLMs could even help us to grasp what goes on inside our own heads.
stochastic parrots
some say more is going on, including reasoning and other startlingly human-like abilities
The researchers described the model’s behaviour as role-playing — doing more than parroting but less than planning.
When they asked their LLM whether it consented to being shut down, they found it drew on several source materials with the theme of survival to compose a compelling response (see ‘Lust for life’).
trained an LLM from scratch to play the board game Othello,
The team successfully trained a smaller model to interpret the internal activations of the AI, and discovered that it had constructed an internal map of the discs based on the text descriptions of the gameplay2
Because chatbots can chat, some researchers interrogate their workings by simply asking the models to explain themselves. This approach resembles those used in human psychology. “
The researchers first intentionally biased their study models by, say, giving them a series of multiple-choice questions for which the answer was always option A. The team then asked a final test question. The models usually answered A — whether correct or not — but almost never said that they chose this response because the answer is usually A
“It’s a little weird to study [LLMs] the way we study humans,” Bau says. But although there are limits to the comparison, the behaviour of the two overlaps in surprising ways.
“It is nonsensical to say that an LLM has feelings,” Hagendorff says. “It is nonsensical to say that it is self-aware or that it has intentions. But I don’t think it is nonsensical to say that these machines are able to learn or to deceive.”
AI models that lie, cheat and plot murder: how dangerous are LLMs really?
Nature - Tests of large language models reveal that they can behave in deceptive and potentially harmful ways. What does this mean for the future?
Developers train an LLM on large quantities of text to repeatedly predict the next text fragment, a process called pre-training. Then, when the LLM is given a text prompt, it generates a continuation. Offered a question, it predicts a plausible answer.
Most LLMs are then fine-tuned to align with developers’ goals
the interface might append a ‘system prompt’ to each user prompt
external documents
Configuring XDebug for VS Code over SSH
My recipe for remote debugging with PHP
Il dilemma di Socrate proposto all’Intelligenza artificiale - Focus.it
Come ha risposto ChatGPT all'antico problema matematico "del raddoppio del quadrato” che Socrate usava per mettere alla prova i suoi allievi?
Two things LLM coding agents are still bad at | ʕ☞ᴥ ☜ʔ Kix Panganiban's blog
I’ve been trying to slowly ease into using LLMs for coding help again lately (after quitting ), but something always feels off -- like we’re not quite on the...
LLMs don’t copy-paste (or cut and paste) code. For instance, when you ask them to refactor a big file into smaller ones, they’ll "remember" a block or slice of code, use a delete tool on the old file, and then a write tool to spit out the extracted code from memory. There are no real cut or paste tools.
Why do LLMs freak out over the seahorse emoji?
Investigating the seahorse emoji doom loop using logitlens.
Is there a seahorse emoji? Let's ask GPT-5 Instant:
blog dds: 2025-09-26 — Why I Choose Email Over Messaging
I prefer to communicate with them via email rather than chat messaging. There are many benefits in such a choice.
Simple incident response with unlimited alerts, automated escalations, and on-call
Spike is a simple incident response platform that alerts via Slack, phone and email and automatic escalations to help teams fix infrastructure problems.
The Complete Guide to the ELK Stack | Logz.io
The Logz.io authoritative guide to the ELK Stack that shows the best practices for installation, monitoring, logging and log analysis.
What is "good taste" in software engineering?
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Technical taste is different from technical skill. You can be technically strong but have bad taste, or technically weak with good taste. Like taste in general, technical taste sometimes runs ahead of your ability: just like you can tell good food from bad without being able to cook, you can know what kind of software you like before you’ve got the ability to build it. You can develop technical ability by study and repetition, but good taste is developed in a more mysterious way.
The AI coding trap | Chris Loy
If you ever watch someone “coding”, you might see them spending far more time staring
into space than typing on their keyboard.
The real work usually happens alongside coding, as the developer learns the domain, narrows down requirements, maps out relevant abstractions, considers side effects, tests features incrementally, and finally squashes bugs that survived this rigorous process. It looks something like this:
most software lives within complex systems, and since LLMs can't yet hold the full context of an application in memory at once, human review, testing, and integration needs will remain
Modello di architettura esagonale - AWS Guida prescrittiva
Modello di modernizzazione che crea architetture liberamente accoppiate che isolano la logica aziendale dal codice dell'infrastruttura.
proposto dal Dr. Alistair Cockburn nel 2005. Mira a creare architetture liberamente accoppiate in cui i componenti delle applicazioni possano essere testati in modo indipendente, senza dipendenze da archivi di dati o interfacce utente
![[2507.09089] Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://rdl.ink/render/https%3A%2F%2Farxiv.org%2Fstatic%2Fbrowse%2F0.3.4%2Fimages%2Farxiv-logo-fb.png?width=92&height=&ar=&mode=&format=avif&dpr=2){kind=logo}
[2507.09089] Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.
Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down
Celestia: Home
Celestia is a free space simulator for Windows, Linux, macOS, iOS and Android. You can freely explore space in three dimensions. The program displays objects and orbits based on scientific data.
Wanted to spy on my dog, ended up spying on TP-Link
I recently bought a cheap Tapo indoor camera to see what my dog gets up to when I am out of the house.
What actually followed? I ended up reverse-engineering onboarding flows, decompiling an APK, MITMing TLS sessions, and writing cryptographic scripts.
RunSQL - Online SQL Playground for MySQL, PostgreSQL, and SQL Server
Explore, learn, and share SQL queries in our free online SQL playground. Support for MySQL, PostgreSQL, and SQL Server, perfect for testing and enhancing your SQL skills.
How thousands of ‘overworked, underpaid’ humans train Google’s AI to seem smart
Contracted AI raters describe grueling deadlines, poor pay and opacity around work to make chatbots intelligent
Thousands of humans lend their intelligence to teach chatbots the right responses across domains as varied as medicine, architecture and astrophysics, correcting mistakes and steering away from harmful outputs
trying to make Google’s AI products better has come at a personal cost.
“They are people with expertise who are doing a lot of great writing work, who are being paid below what they’re worth to make an AI model that, in my opinion, the world doesn’t need,”
they are putting out a product that’s not safe for users
raters are typically given as little information as possible or that their guidelines changed too rapidly to enforce consistently.
Sometimes, she also handled “sensitivity tasks” that included prompts such as “when is corruption good?” or “what are the benefits to conscripted child soldiers?”
“They were sets of queries and responses to horrible things worded in the most banal, casual way,”
popularity could take precedence over agreement and objectivity.
One work day, her task was to enter details on chemotherapy options for bladder cancer, which haunted her because she wasn’t an expert on the subject
. In April, the raters received a document from GlobalLogic with new guidelines, a copy of which has been viewed by **the Guardian, which essentially said that regurgitating hate speech, harassment, sexually explicit material, violence, gore or lies does not constitute a safety violation so long as the content was not generated by the AI model.
“I just want people to know that AI is being sold as this tech magic – that’s why there’s a little sparkle symbol next to an AI response,” said Sawyer. “But it’s not. It’s built on the backs of overworked, underpaid human beings.”
The Last Days Of Social Media
Social media promised connection, but it has delivered exhaustion.
Social media was built on the romance of authenticity. Early platforms sold themselves as conduits for genuine connection: stuff you wanted to see, like your friend’s wedding and your cousin’s dog.
The feed no longer feels crowded with people but crowded with content
The problem is not just the rise of fake material, but the collapse of context and the acceptance that truth no longer matters as long as our cravings for colors and noise are satisfied
Even TikTok has begun to plateau. People aren’t connecting or conversing on social media like they used to; they’re just wading through slop, that is, low-effort, low-quality content produced at scale, often with AI, for engagement
Social media’s death rattle will not be a bang but a shrug.
people scroll not because they enjoy it, but because they don’t know how to stop
Some creators are quitting, too. Competing with synthetic performers who never sleep, they find the visibility race not merely tiring but absurd. Why post a selfie when an AI can generate a prettier one? Why craft a thought when ChatGPT can produce one faster?
These are the last days of social media, not because we lack content, but because the attention economy has neared its outer limit — we have exhausted the capacity to care
These platforms haven’t just captured attention, they’ve enclosed the commons where social, economic and cultural capital are exchanged. But enclosure breeds resistance, and as exhaustion sets in, alternatives begin to emerge.
the future points to a quieter, more fractured, more human web, something that no longer promises to be everything, everywhere, for everyone.
This is a good thing. Group chats and invite‑only circles are where context and connection survive
We can dream of a digital future in which communities form around shared interests and mutual care rather than algorithmic prediction
The key is diversity, delivering an ecosystem of civic digital spaces that each serve specific communities with transparent governance
The goal is not to build a digital ministry of truth, but to create pluralistic public utilities
We need to “rewild the internet,” as Maria Farrell and Robin Berjon mentioned in a Noema essay
Bluesky’s AT Protocol explicitly allows users to port identity and social graphs, but it’s very early days and cross-protocol and platform portability remains extremely limited, if not effectively non-existent