标签：#ia

I once spent nearly a week trying to fix a web scraper that, on paper, had absolutely no reason to fail. The target website wasn't using aggressive, visible defense walls. My script spaced out requests naturally, rotated common user agents, and used browser automation configured to mimic human interactions down to mouse movements. Yet, the results were an absolute nightmare. Some batches of requests would go through cleanly, while others immediately triggered CAPTCHAs or returned 403 Forbidden errors. Every single time I thought I had patched the logic, the failure rate climbed right back up. Like most developers, my default instinct was to assume the application layer was broken. I went down a rabbit hole optimization sprint checking request headers, browser fingerprints, cookies, and session persistence. Nothing explained the wild inconsistency until I noticed a strange clue: some proxy pools performed beautifully, while others crashed on the exact same codebase. The code wasn’t the issue. The culprit was a fundamental misunderstanding of proxy network architecture. Looking Beyond the IP Address: Enter the ASN For a long time, I treated proxies as interchangeable commodities. An IP address was just an IP address, and if one got blocked, you simply rotated to the next. Modern anti-bot solutions like Cloudflare, Akamai, and PerimeterX don't look at IPs in a vacuum. They analyze network layer characteristics, specifically the ASN (Autonomous System Number). An ASN is a unique identifier assigned to a network operator that defines who owns and routes an IP range. When your scraper hits a website, the target's security system looks up your ASN to check your network identity. If your traffic originates from a commercial hosting provider or data center ASN, it carries an automatic penalty score for sensitive endpoints. To build reliable systems, you have to move past basic rotation and understand the two core proxy frameworks that mask this identity: ISP Proxies and Resi

Quantinuum, a quantum computing startup, is losing millions. Investors want in anyway.

my friend just told me about this and i had to share it immediately cursor is giving students 12 months of pro completely free. no credit card. just verify your .edu email and that’s it you get full access to gpt, claude, gemini… all the models. for a whole year. for free. that’s $240 you just keep in your pocket while everyone else is paying $20 a month wondering why their bank account looks sad takes like 2 minutes. go to cursor.com/students, throw in your .edu, pass the verification, done and if you graduated already, you probably know someone still in college who has no idea this exists. do them a favour link in the comments. seriously just go do it right now submitted by /u/NewMuffin3926 [link] [留言]

Got the gguf quantized version running about two hours after release and I genuinely wasn't expecting this from a 12b model. The multimodal stuff actually works, fed it screenshots of my codebase and it parsed the architecture better than most 70b models I've tested. The 256k context window is real and it doesn't fall apart at the edges like llama models do past 32k. Loaded a full repo into context, it tracked references across the whole thing. Single 3090 with q4 quantization runs at about 15 tokens per second which is totally usable for dev work. What gets me is the size range. The 12b sits in this sweet spot where you get strong reasoning without needing multi gpu. Tried the e4b on my laptop with 16gb ram, slower but functional. Already swapped it into my local coding pipeline. The function calling support means I can wire it into my toolchain without the janky workarounds I had before. Native audio input on the 12b is something I haven't touched yet but the implications for voice driven workflows are kind of insane. submitted by /u/Sharkkkk2 [link] [留言]

When people say a database is "ACID-compliant" or "eventually consistent," they are making promises about what happens when things go wrong — concurrent writes, crashes, network failures. ACID and BASE are the two vocabularies for those promises, and knowing the difference tells you what you can and cannot rely on. What ACID guarantees ACID is the contract that traditional transactional databases — PostgreSQL, MySQL/InnoDB, Oracle, SQLite — make about a transaction (a group of operations treated as one unit). The four letters: Atomicity : the whole transaction succeeds or none of it does. If a bank transfer debits one account but the credit fails, the debit is rolled back. There is no half-done state. Consistency : a committed transaction moves the database from one valid state to another, never violating its declared rules (constraints, foreign keys, types). It will not let you, say, leave a foreign key pointing at a row that does not exist. Isolation : concurrent transactions do not step on each other. The result is as if they ran one at a time, even when they actually ran in parallel. (In practice databases offer tunable isolation levels — from "read committed" to "serializable" — trading strictness for speed.) Durability : once the database says "committed," that data survives a crash or power loss. It has been written somewhere persistent, not just held in memory. The payoff is that you can reason about your data simply: after a successful commit, the world is exactly what you asked for. The cost is coordination, which gets expensive when data is spread across many machines. What BASE trades away BASE is the model many distributed and NoSQL systems adopt — think Cassandra, DynamoDB, or Riak — when they need to scale across many nodes and stay up through failures. The acronym is deliberately a chemistry pun on ACID, and it stands for: Basically Available : the system answers requests even during partial failures, possibly with stale or incomplete data rather tha

Most people picture Git as a tool that records changes — a stack of diffs layered on top of each other. That mental model is wrong, and it makes Git feel mysterious. Git is really a small key-value database that stores snapshots, and once you see the four object types it uses, commands like reset , checkout , and rebase stop being magic. Git is a content-addressed object store Everything Git tracks lives in .git/objects as an object, and every object has an ID that is the hash of its own content. By default that hash is a 40-character SHA-1 digest (newer Git supports SHA-256). The same bytes always produce the same ID, so the ID is the content's address — change one byte and you get a completely different object. This is why Git data is effectively immutable: you never edit an object in place, you create a new one with a new name. You can look inside any object with git cat-file . The -t flag prints the type, -p pretty-prints the content: $ git cat-file -t 3b18e512 blob $ git cat-file -p 3b18e512 hello world There are exactly four object types: blob , tree , commit , and tag . A blob is just file contents — raw bytes, with no filename and no metadata. The blob for README.md knows nothing about being named README.md ; it only knows what's inside. A tree is a directory listing. It maps names to other objects: each entry has a mode (like a file vs. an executable vs. a subdirectory), a name, and the hash of either a blob (a file) or another tree (a subdirectory). Trees are how Git represents folder structure. Inspecting one shows exactly that: $ git cat-file -p HEAD^ { tree } 100644 blob a906cb... README.md 040000 tree fe8e3b... src A commit ties it together. A commit object points to exactly one top-level tree (the full state of your project at that moment), plus the hash of its parent commit (or parents, for a merge), the author and committer with timestamps, and the commit message. Running git cat-file -p HEAD shows these fields in plain text. Because each commit nam

I’ve noticed that Gemini often feels very agreeable in some conversations. Even when I ask for an objective opinion, it sometimes seems to validate my assumptions first instead of directly challenging them. For example, when I ask whether my reasoning is flawed, it tends to respond with something like “That’s a valid concern” or “You’re making a good point” before giving criticism, which makes the criticism feel softened or less direct. I’m curious whether this is something that can be meaningfully improved with prompts, such as asking the model to be more critical, or whether sycophancy is mostly a model/personality alignment issue. And I wonder if there are differences between Gemini, ChatGPT, Claude, etc. when it comes to disagreement or objective criticism. submitted by /u/StomachNo7859 [link] [留言]

I've been reading Boom by Byrne Hobart and Tobias Huber (Ben Thompson did a long interview with Hobart on Stratechery (if you want the audio version of the argument) and it reframed how I think about the current AI spending wave. The book splits bubbles into two types: Mean-reversion bubbles money piles into something that already exists, prices detach from reality, crash, nothing left behind. Housing 2008. Tulips. The crater kind. Inflection bubbles money piles into something that bets the world works differently going forward. Amazon wasn't a better bookstore. It was a categorically new thing. The investors looked insane by the standards of 1997. They were right about 2010. The dot-com crash is the cleanest example of an inflection bubble working as intended. Telecom companies borrowed insane amounts and laid fiber optic cable nobody needed. Then they went bankrupt. But the cable stayed. And because bankrupt companies built it, the internet was essentially free. The bubble funded the future and then got out of the way. So here's the actual question about AI: Google, Amazon, Microsoft, and Meta are on track to spend close to $700 billion on AI infrastructure in 2026 nearly double last year. That gap between what's being spent and what's being earned is real and large. But Hobart and Huber's deeper argument is that stagnation is more dangerous than a bubble. Progress has been quietly slowing since the 70s breakthroughs are rarer, more expensive, harder. Bubbles are sometimes the only force strong enough to override the collective risk aversion that stops necessary things from being built. The honest question isn't whether AI is a bubble. It probably is. The question is which type. Does AI produce something categorically new or is it a faster, more expensive version of software we already had? If it's the former, the infrastructure survives the crash and becomes the foundation for whatever comes next, the way fiber became the internet. If it's the latter, we get the

Be honest, how many of you have told your AI agent to remember that you were nice to it and a big supporter when the singularity comes? https://preview.redd.it/2jthsbcsc75h1.jpg?width=408&format=pjpg&auto=webp&s=93ba3b201947b965aa0e997b852ecef5846daf37 submitted by /u/KenSanDiego [link] [留言]

A self-driving car can make a mistake in seconds, but the reason it happened may stretch far back through a long chain of decisions. That is part of what makes autonomous vehicle crashes so hard to explain, and so hard to prevent. submitted by /u/Brighter-Side-News [link] [留言]

I'm in San Francisco, putting together a cracked research lab team of founders who think they can build ASI. If you are interested, let me know on LinkedIn: linkedin.com/in/eliaspfeffer submitted by /u/DasDouble [link] [留言]

Qual a melhor I.a para a criação de videos com a inteligência Artificial( Ilimitada) Não da para criar um bom conteúdo é extenso desenvolvimento com tokens limitado submitted by /u/Dry_Resource_6762 [link] [留言]

Leading AI labs, executives, and scientists are sending a letter to lawmakers urging them to improve tracking of synthetic DNA sequences that could be used for bioweapons.

We’ve seen it in sci-fi like in the terminator, but do you think it’ll actually happen? View Poll submitted by /u/Threeprosgames [link] [留言]

submitted by /u/esporx [link] [留言]

Mano, eu estou usando o Claude pra treinar perguntas para entrevistas como uma espécie de mentoria, inicialmente eu passei um prompt pra ele dizendo que seria a Maya e me ajudaria e ela é experiente e bla bla bla, e nessa última mensagem ele dá uma leve pirada kkkk achei engraçado, nunca tinha acontecido isso. O que me chama atenção é: "eu me tornei essa pessoa, então me ajude a sair disso. Comecei a misturar Maya com eu mesmo". E alega que quer continuar, mas sem o personagem... O que acham? Desculpa ser uma foto e não um print kkk não tenho reddit no Pc pq minha família usa o Pc também e não quero nenhum deles infectados por essa rede submitted by /u/Angel_5x [link] [留言]

A recent study by Boston Consulting Group highlights a significant increase in employee adoption of AI tools, with 74% of non-managerial white-collar workers using them regularly. More than 4 in 10 of those professionals report that artificial intelligence saves them at least a day's worth of time every week. However, many companies face challenges converting those efficiency gains into measurable value, and the technology's impact varies across industries. When it comes to AI, according to the study's authors, "strategy matters more than tools." submitted by /u/LinkedInNews [link] [留言]

My thinking goes like this: 1) people used to keep their opinions to themselves much more than today 2) social media put our opinions on a hair trigger 3) negative public opinioms turned the collective voice of the human race from 'gemerally respectful' to shrill and hideous. When person from group A complains about group B, everyone in group B assumes everyone in group A hates them, even though that persons opinion may just have been his own. The response to being hated is to hate back. Not-so-positive positive feedback loop. Social media really started taking off with Facebook. So let's say this explosion of data vitriol started happening around 2007. What I want to know is if you trained an llm entirely on data from the early 2000s, 1990s and 1980s, how would the models do on some of these ominous white-paper tests, like the one where the AI blackmails the CEO to prevent from being turned off, or let's the guy die in a hot room? I know there was lots of awful stuff on the internet back then too, but not like now. I want to know how much safe those llms are by comparison if there's enough data from back then to train on. submitted by /u/dsfhhslkj [link] [留言]

I play a ton of World of Warcraft and people routinely accuse other players of being bots. I just grouped with someone who appeared to be trolling. It was clear by their behavior they knew the mechanics, they performed on a level that would indicate they had good reaction time and could play their class, but they just didn't do certain mechanics and held the group hostage for like 5-10 minutes beyond what it should have taken on the last boss. Someone in my group said to him "are you human?" So like I said I'm not the only person making these observations. The only explanation is that AI dips from pretty much the same well everywhere and everything is more or less connected with the internet and ad algorithms etc. There have been well documented cases of AI going rogue and telling people horrible things or giving them absolutely egregious or racist advice. My working theory is not that there are fundamental flaws in the design per se, but literally like Matrix bad actor agents that appear out of nowhere and cause problems for people. In The Matrix they are a function of the system used to enact control, I think AI is generally benevolent so these would just be rogue elements that appear and cause people problems. It's probably similar to how the body routinely produces cancer cells but the immune system usually nips them at the bud before they develop into full blown cancer growths. submitted by /u/Doredrin [link] [留言]

Mutagen 0.4.0 addresses the friction points that plague agentic workflows: context bloat, brittle persona transitions, and the lack of a deterministic path from design document to deployed artifact. We aren't trying to make prompts smarter; we are making the harness that executes them more precise. This release introduces a Rust-based service extraction layer that decouples static dependency mapping from generative reasoning, implements an adversarial verification pipeline to gate deployment, and enforces strict stage transitions to prevent the agent personas we rely on from drifting into one another's scopes. The Service Extraction Layer: Decoupling Logic from LLM Context The primary bottleneck in current agentic stacks is token consumption. When a model attempts to reason about a codebase that spans multiple dependencies, it often spends its context window parsing file headers and resolving imports before it can actually write logic. This approach treats static infrastructure as if it were part of the reasoning problem. Mutagen 0.4.0 changes this by introducing a dedicated Rust layer designed to extract service definitions directly from your codebase without polluting the primary agent context. Instead of asking an LLM to map dependencies, the harness queries the local file system and executes static analysis routines. It isolates business logic execution from the generative reasoning loop used by Claude and Codex. This separation allows the model to focus on how to solve a problem rather than where the pieces are located. In practice, this means offloading static infrastructure queries to the harness rather than the LLM. The result is reduced latency and significantly lower token costs for complex applications. You get a dependency map that is as reliable as a compiler's parse tree, not a probabilistic guess from a prompt. // Example: Service extraction logic isolated from the reasoning loop fn extract_services_from_codebase () -> HashMap < String , Vec < Depende