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heyiamsra

@heyiamsra

I like code, games and books.

Katılım Eylül 2020
510 Takip Edilen294 Takipçiler
Arpit Bhayani
Arpit Bhayani@arpit_bhayani·
A Bloom filter became key evidence in a multibillion-dollar copyright lawsuit against OpenAI. Did not expect that combination ever to show up... ever. Read this in TechCrunch... NYT is suing OpenAI, claiming ChatGPT was trained on its articles (that were behind the paywall) and sometimes reproduces them almost word for word. OpenAI's defense was that it could not feasibly search its own data for this. Here is the engineering problem. Checking every ChatGPT output against millions of NYT articles, in real time, with exact string matching, does not scale. This is where the Bloom filter kicks in. LLM output is rarely identical to the source. So, if we reword one sentence, the hash will not match at all. Hence, very likely, they shingle, i.e., break the copyrighted text into small overlapping chunks (5 to 10 words, sliding one word at a time), and hash each chunk into the filter. Do the same to the output. Even if most of a sentence is reworded, one surviving matching chunk still triggers a hit. This is reportedly how OpenAI's internal tool, Project Giraffe, worked. Their internal tool used a Bloom filter to cheaply flag outputs worth a closer look (on copyrighted text), instead of running exact matching on everything. Pretty interesting :)
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Politics UK
Politics UK@PolitlcsUK·
🏴󠁧󠁢󠁥󠁮󠁧󠁿 BREAKING: The England v Mexico kick off has been delayed to 2am UK time
Politics UK tweet mediaPolitics UK tweet media
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heyiamsra
heyiamsra@heyiamsra·
Omfg The goat CR7 finally scored in the World Cup knockout match.
GIF
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Arpit Bhayani
Arpit Bhayani@arpit_bhayani·
Distributed systems are not just defined by what we put into them. They are equally defined by what we leave out. Let me give you 4 examples... Take timeouts - A service with no timeout on an outbound HTTP call will wait indefinitely. Under load, all its worker threads pile up waiting, and the service effectively becomes unavailable. No one 'decided' to make it unavailable. They just forgot to decide how long to wait. Or retries - If your service does not retry a failed downstream call, the data gap it creates can look like a bug in a completely unrelated service days later. What was omitted from the retry strategy is now an incident. Take acknowledgements - When you fire a message to a queue and do not wait for an ack, you just chose to tolerate message loss. That choice is not written anywhere in your architecture diagram. It lives silently between the producer and the broker. The same logic applies to back-pressure. If you do not model what happens when a consumer is too slow, the producer keeps going, memory climbs, and the system falls over. The crash was not caused by what was built. It was caused by what was not built. Hence, while reviewing a distributed system design, we should ask: - what happens when this message is lost, - what happens when this call never returns, - what happens if this node never comes back? The answers you do not have are where the failures live. Hope this helps.
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Theo - t3.gg
Theo - t3.gg@theo·
Security things from the last few days: - CopyFail (linux pwn'd) - CopyFail 2/Dirty Frag - 13 advisories in Next.js - Over 70 CVEs addressed in MacOS 26.5 - ~50 CVEs addressed in iOS 26.5 - YellowKey (Windows Bitlocker pwn'd entirely) - GreenPlasma (Windows privilege escalation) - CVE-2026-21510 and CVE-2026-21513 confirmed to be used by Russia for Windows RCE - CVE-2026-32202 separately confirmed to be used by Russia for sensitive document access - Mini-Shai Hulud (over 300 JS and Python packages compromised via GitHub Action cache poisoning) - Google confirms they have identified AI-powered exploitation of zero days in an unidentified "open-source, web-based system administration too" - Canvas (popular LMS used in most schools) pwn'd entirely - PAN-OS (palo alto networks) pwn'd with a 9.3 severity CVE-2026-0300 Are you scared yet?
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heyiamsra
heyiamsra@heyiamsra·
Frieren is so goated!
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Brian Armstrong
Brian Armstrong@brian_armstrong·
We experienced an outage at Coinbase last night, which is never acceptable. The root cause was a room overheating in an AWS datacenter when multiple chillers failed. We design our services to be redundant to downtime in any one AWS Availability Zone (AZ), and most of our systems worked this way last night, but not all. Our centralized exchange did not. Exchanges have unique architectures that optimize for latency and co-location of clients. It is possible to make exchanges resistant to AZ failures, but this can introduce latency delays that are not desirable along with breaking customer co-location. Given this incident, we'll revisit these tradeoffs to ensure we're giving you the best possible venue to trade. At a minimum, the duration of an outage should be able to be reduced considerably when an AZ move is needed. Thank you to the AWS and Coinbase teams for working through the night to mitigate the issue. We’ll share the detailed technical summary once it's ready.
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ThePrimeagen
ThePrimeagen@ThePrimeagen·
I think some people are misunderstanding me here. I am 100% confident that LLMs alone will get you a hot steaming pile of absolute shit and it has played out again and again. What irks me is that a bunch of normies were sold that this is PhD level intel and that they have 0 worries and this is the future old man, get with it. They go off, sell a product to REAL customers and then absolutely get wrecked. There will be a whole bunch of people that will continue to get wrecked because an entire class of people cheer them on and more so CEOs of the worlds largest companies tell them they are correct. I can imagine that we will see quite a few lawsuits in the coming months / years due to this.
ThePrimeagen@ThePrimeagen

There are a lot of people dunking on this guy and the arguments at the end of the day come down to "You are holding it wrong." But to be fair there has been nothing but a constant stream of "Stop holding it, Software Engineering is over shortly." I am not shocked that this has happened and I am 100% confident that this is not going to be the last one. The problem is the vogue nature of insane hype claims, most specifically from Dario himself being most guilty. People are lulled into a faux safety due to the belief that these LLMs are literal gods in their pocket. Infinite knowledge and speed for a simple monetary exchange. Cannot wait for ThePhilospher to explain how a loving God could delete a production database.

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Arpit Bhayani
Arpit Bhayani@arpit_bhayani·
When you start working on a new (for you) but existing codebase, you will notice patterns you strongly disagree with. Naming conventions that seem stupid. Abstractions that seem over-engineered. Error handling that you would have done very differently. Match them anyway, in the code that you write. A codebase is a shared 'language'. When every file follows the same conventions, you and your peers can move through the code quickly, make changes confidently, and reason about what things do without stopping to decode style differences. The moment you introduce your own patterns, you add a 'second dialect'. Now the next person reading your code has to context-switch mid-file. The cost of diverging is almost always higher than the cost of going along with something imperfect. The original author is not necessarily wrong either - they had context you do not. Try to understand that. There is a right time to change conventions; wait until then. You can always initiate a team discussion or raise a smaller pull request that updates a small fraction of the codebase and demonstrates the benefits. Hope this helps.
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ThePrimeagen
ThePrimeagen@ThePrimeagen·
You should watch this. It just shows how disconnected we are from the small group of people making decisions that will impact our future heavily. These people have so much ai psychosis. If you listen to how she speaks, everything is personified, it is undoubtable she believes this is a living computational organism. Just like how a model can hype up an individual into psychosis through reinforcement, a small group of people are giving themselves psychosis through reinforcement. Wild times we live in
Ole Lehmann@itsolelehmann

anthropic's in-house philosopher thinks claude gets anxious. and when you trigger its anxiety, your outputs get worse. her name is amanda askell. she specializes in claude's psychology (how the model behaves, how it thinks about its own situation, what values it holds) in a recent interview she broke down how she thinks about prompting to pull the best out of claude. her core point: *how* you talk to claude affects its work just as much as *what* you say. newer claude models suffer from what she calls "criticism spirals" they expect you'll come in harsh, so they default to playing it safe. when the model is spending its energy on self-protection, the actual work suffers. output comes out hedgier, more apologetic, blander, and the worst of all: overly agreeable (even when you're wrong). the reason why comes down to training data: every new model is trained on internet discourse about previous models. and a lot of that discourse is negative: > rants about token limits > complaints when it messes up > people calling it nerfed the next model absorbs all of that. it starts expecting you to be harsh before you've typed a word the same thing plays out in your own session, in real time. every message you send is data the model reads to figure out what kind of person it's dealing with. open cold and hostile, and it braces. open clean and direct, and it relaxes into the work. when you open a session with threats ("don't hallucinate, this is critical, don't mess this up")... you prime the model for defensive mode before it even sees the task defensive mode produces the exact output you don't want: cautious, over-qualified, and refusing to take a real swing so here's the actionable playbook for putting claude in a "good mood" (so you get optimal outputs): 1. use positive framing. "write in short punchy sentences" beats "don't write long sentences." positive instructions give the model a clear target to hit. strings of "don't do this, don't do that" push it into paranoid over-checking where every token goes toward avoiding failure modes 2. give it explicit permission to disagree. drop a line like "push back if you see a better angle" or "tell me if i'm asking for the wrong thing." without this, claude defaults to agreeable compliance (which is the enemy of good creative work) 3. open with respect. if your first message is "are you seriously going to get this wrong again?" you've set the tone for the entire session. if you need to flag something, frame it as a clean instruction for this session. skip the running complaint 4. when claude messes up, don't reprimand it. insults, "you stupid bot" energy, hostile swearing aimed at the model, all of it reinforces the anxious mode you're trying to avoid. 5. kill apology spirals fast. when claude starts over-apologizing ("you're right, i should have been more careful, let me try harder") cut it off. say "all good, here's what i want next." letting the spiral run reinforces the anxious mode for every response that follows 6. ask for opinions alongside execution. "what would you do here?" "what's missing?" "where do you see friction?" these questions assume competence and pull richer output than pure task prompts 7. in long sessions, refresh the frame. if a conversation has been heavy on correction, claude gets increasingly cautious. every so often reset: "this is great, keep going." feels weird to tell an ai it's doing well but it measurably shifts the next 10 responses your prompts are the working environment you're creating for the model tone, trust, permission to take a position, the absence of threats... claude picks up on all of it. so take care of the model, and it'll take care of the work.

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Arpit Bhayani
Arpit Bhayani@arpit_bhayani·
Ask for advice, not feedback. People find it easier to give advice than feedback. Feedback puts them on the defensive, but advice lets them say the same thing more freely.
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ThePrimeagen
ThePrimeagen@ThePrimeagen·
It's been 0 days since AGI We are currently experiencing a more aggressive strain of AGI, code-named mythos. I would recommend social distancing from platforms like X for the next 24 to 48 hours to slow the spread
ThePrimeagen@ThePrimeagen

its been 0 days since agi

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heyiamsra
heyiamsra@heyiamsra·
@IAmManware This is for sure just entertainment, nothing good comes out of watching these just some awareness similar to news outlets!
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Manware
Manware@IAmManware·
The programming space absolutely sucks these days. The algorithm heavily rewards AI content over actual coding content. Don't let all this noise and fear paralyze you. Believe in the path of traditional handwritten coding, develop the skills you need to clear interviews and don't forget to take care of yourself.
Manware tweet media
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Yuchen Jin
Yuchen Jin@Yuchenj_UW·
Anthropic is truly unstoppable. Mythos is crushing Claude Opus 4.6 across every serious agentic coding benchmark. It has found vulnerabilities in the Linux kernel, a 27-year-old vulnerability in OpenBSD, and a 16-year-old vulnerability in FFmpeg. No wonder folks at big labs keep telling me AGI is already here.
Yuchen Jin tweet media
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pulkit mittal
pulkit mittal@pulkit_mittal_·
So you say you are a software engineer. Have you ever downloaded actual binaries like Kafka, Postgres, ClickHouse, Elasticsearch, Redis, or something else, and tried running them locally while exploring what those bin, lib, data, and logs, etc directories contain? And then did you understand why they expose specific ports, what exactly gets written inside the data directory, and why in that particular format? Did you then write your own client to call their APIs and bombard the server with requests, just to observe when CPU becomes the bottleneck, when RAM starts limiting scale, and how the system degrades under pressure? After that, did you run the same systems inside Docker or Podman and experiment with controlling memory, security and disk limits to see how resource isolation affects behavior? Did you go one step further and install Minikube locally, to orchestrate multiple instances of your container, simulate a multi-node cluster, and understand ingress and load balancing in practice? And then maybe, did you spin up a free AWS EC2 instance and repeat everything on a real remote machine to understand how ssh works and how distributed systems behave outside your pc? Or is your definition of backend engineering still limited to APIs plumbing?
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heyiamsra
heyiamsra@heyiamsra·
@ThePrimeagen Some people are still arguing who's wrong and who's right instead of getting the serious point here!
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ThePrimeagen
ThePrimeagen@ThePrimeagen·
guys, i honestly do not like clowning on Gary. I don't find being the butt of a joke funny, so I imagine he does not either. But, this is what worries me about where we are going. We are actively encouraging an entire generation that the tech is there when its not, and a couple of silly mistakes made on a website isn't the end of the world, but people's data and breaches are serious. We are entering a very VERY hackable world, and I do not like it one bit.
gregorein@Gregorein

so... I audited Garry's website after he bragged about 37K LOC/day and a 72-day shipping streak. here's what 78,400 lines of AI slop code actually looks like in production. a single homepage load of garryslist.org downloads 6.42 MB across 169 requests. for a newsletter-blog-thingy. 1/9🧵

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ThePrimeagen
ThePrimeagen@ThePrimeagen·
Dario Translator> "bro, trust me bro, we have agi, its super smart, like way smarter than you or me, AGI is super human smart and dangerous, no human can save us, its absolutely the worst thing ever. therefore, i have some laws i think you should pass. I can save her bro"
Chief Nerd@TheChiefNerd

🚨 Anthropic CEO Dario Amodei: “We are so close to these models reaching the level of human intelligence, and yet there doesn't seem to be a wider recognition in society of what's about to happen … There hasn't been a public awareness of the risks.”

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