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BROP is such a clean ASLR bypass. After brute-forcing the stack canary, the attacker starts overwriting the return address with guesses. Most guessed addresses jump into garbage or non-executable memory. The child process segfaults, and the socket closes. But occasionally, a guessed return address lands in real code that...

25,142 views • 3 months ago •via X (Twitter)

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HE MAKES MONEY IN REAL ESTATE WITHOUT BUYING, SELLING, OR EVEN SEEING A SINGLE HOUSE. HERE'S THE EXACT SETUP He never owns a property. He takes a single listing, turns it into a polished 30-second video, and sells that to the agent who posted it. Realtors need video for their feeds and almost none of them can make it. He sits in the middle and builds the whole thing once as a skill that runs on command Here is the exact process: 1. Pull the listing. Go to Zillow, open any listing, download the high-res images, and grab the property info. That is your raw material 2. Turn photos into video with Google Veo. Get a Google API key for Veo, the image-to-video model. It takes the listing photos and animates them into clean 30-second footage. This is the best one out right now 3. Add the voice with ElevenLabs. Get an ElevenLabs API key. Feed it the listing details and it returns a voiceover that sounds like a real human, not a robot. Lay it over the video with the text on screen 4. Send it with AgentMail. Get an AgentMail key so the system can send the finished email out on its own Then you wire it into one skill. Scrape the listing, send images to Veo, add the ElevenLabs voiceover and on-screen text, then send the email. Feed it each key one at a time and have it build each step Who you sell to: Pull realtors off Zillow and Realtor com whose listings have flat photos and zero video. That gap is your pitch. Send a free sample made from their own listing first, then charge a monthly rate for ongoing clips. One agent with ten listings is a recurring client, fully online Bookmark this

Yarchi

106,174 views • 3 months ago

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,719 views • 2 months ago

This scene always makes me think about what markets do once a simple asset story is no longer enough. At first people buy the thing itself. Then, at some point, that stops being exciting enough. So the market starts building layers around it. A structure. A wrapper. A more financial version of the same idea. That is why MSTR comes to mind for me here. Not because it is the same as a synthetic CDO. It obviously is not. But because the instinct feels familiar. If you believe in Bitcoin, the clean path is simple. You buy Bitcoin. If you want a more traditional route, you buy the ETF. That should be enough. But MSTR is something else entirely. It is Bitcoin, turned into a corporate vehicle, then turned into a capital markets machine, then turned into a narrative people are willing to value differently from the asset underneath it. And that is where it starts feeling less like pure exposure and more like financial engineering built around exposure. We have seen this movie before in different forms. Markets love taking a real asset, wrapping it in a new story, and then assigning the wrapper a value that starts drifting away from the thing it actually holds. As long as that premium stays alive, the machine keeps working. More demand for the structure. More capital raised. More accumulation. Bigger story. More believers. On the way up, it all looks brilliant. That is always the seductive part. In strong markets, even very simple reflexive loops can look like genius. The real question only shows up later, when the mood changes and people start asking whether the value was in the underlying asset all along, or in the extra meaning the market temporarily assigned to the structure built around it. That is why I find this clip so relevant. It is not just a reminder of 2008. It is a reminder of a deeper market habit. People rarely stop at owning the thing. They almost always find a way to build another layer on top of it. And to me, that is the more interesting question around MSTR. Not how much more Bitcoin it can buy. But how long the market will keep rewarding the wrapper more than the thing inside it.

Mercek

14,644 views • 4 months ago

Someone ran Claude Code on a beach where any device overheats and that spot suddenly turned out to be the best home for the most powerful AI in the world. This is the reMarkable Paper Pro. A paper tablet for notes with no browser and no social media and not a single app. He sat down right on the sand in the open sun and brought up Claude Code on Opus 4.6 over the Claude API on the paper screen and opened his project ~/repos/webs while the waves broke a few steps away. For years every device had the same trouble outside. In direct sun the screen glares and washes out and heats up and instead of your work you see your own reflection. But e-ink does not blast its own light into your face. It reflects the sunlight like the page of a book. And here is what came out of it. The very thing that kills any normal screen outside turned into fuel for this one. The brighter the sun the sharper the picture because it has nothing to glare with and nothing to wash out. And then comes the thing no laptop on a beach will give you. Your eyes do not get tired. You can watch Opus think on max effort for an hour and it reads like a book in the sun and not a backlight you squint into. The picture only comes alive. In bright light it does not fade but turns sharper and higher in contrast than it ever was in a room. The charge lasts for days. E-ink barely touches the battery so there is no outlet anywhere on the sand and the tablet does not care. It weighs as much as a notebook. The whole setup folds into a beach bag like a pad with a pen on top. Everything on the screen is for real. Claude Code v2.1.110 and Opus 4.6 on the Claude API and the project ~/repos/webs open right on the e-ink in the middle of the sand. In my opinion this is the most unexpected home for an AI this year. Not an office with the blinds drawn and not a monitor cranked to full brightness but a quiet sheet of paper on the sand that open sun only makes better and on it the most powerful Claude writes code right on the page like a pen.

Blaze

89,297 views • 2 months ago

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.

DegenCalls

20,124 views • 1 month ago

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

Hanako

14,325 views • 25 days ago

This woman is being accused of being rude by the worker, to me I think the worker was rude. She’s trying to return something without the packaging and was denied without it. So she went and bought another one and used that packaging to return the old one. She always had the receipt she just didn’t have the package. Either way she’s left with another item she didn’t need or return. I recall when I was at the pizza shop and a customer gave us the wrong address by mistake. She was babysitting and wanted pizza delivered to the customers house instead. I get a call from our driver saying he couldn’t find it, so I call her back and said that the driver couldn’t find the address, it didn’t even exist on gps. She argues with me how the home has been there for over 80 years and that my driver was too stupid to find it. I couldn’t find it on my Apple Maps either and asked her to double check the address. She told us the house number was 869 and it was actually 896. So after that I call my driver and he delivers it. Not even 5 minutes later she calls back complaining her food was cold. I told her it’s cold likely because the driver took a long time trying to find an address that didn’t exist. So once again she gets all belligerent. I refused to remake it or refund it based on her mistake and she claims I just lost a life long customer. Was I wrong for that, would you have remade it? Maybe I would have as a courtesy if she was cool about it but she wasn’t especially since it was her mistake. I usually don’t do anything unless we messed up.

SonnyBoy🇺🇸

137,875 views • 1 month ago

THIS GUY BUILT A CLAUDE CODE X OBSIDIAN MAP OF HIS ENTIRE CONTENT SYSTEM This is the useful version of “AI second brain.” Not dumping more notes. Not asking Claude for a prettier folder system. Not making a canvas because it looks smart. In the video, he points Claude Code at his Obsidian setup and shows a visual map of the actual content pipeline: Analysis Ideation Prep Scripting Prep Performance The interesting part is the shape. Each stage is connected to the next one. Some boxes show sub-processes. One section shows a router detecting content type and routing a short into the next step. There are references attached to the flow. That is a real payoff: you stop treating your vault like storage and start treating it like an inspectable machine. The move is simple: 1. Put the real workflow in markdown 2. Let Claude Code inspect the vault 3. Ask it to find stages, dependencies, and missing links 4. Turn the output into an Obsidian map 5. Use the map to see what is manual, duplicated, or broken This works because Claude Code is not just summarizing a note. It can read across prompts, docs, scripts, references, and messy process files, then expose the structure you stopped seeing. That is why the demo hits. The video is not really about “better note-taking.” It is about making your private operating system visible enough to debug. Caveat: a beautiful graph does not mean you have a working system. If the notes are vague, the map will be vague. If the process is fake, Claude will draw a fake process very cleanly. If nothing feeds back into performance, the canvas is just decoration. But if the vault already contains real work, Claude Code x Obsidian becomes a powerful audit tool. Your notes stop being a pile. They become a map of what you actually do.

kocer

15,941 views • 2 months ago

context engineering vs graph engineering. every few months the list gets a new word and everyone treats it as a replacement for the last one. these two are not on the same list. one decides what the model sees this turn, the other decides what exists at all. the cleanest way to tell them apart is to ask what a single unit of work looks like. > context engineering is the window the window opens empty, every single time. you assemble what goes in it. the prompt, the docs, the history, the tool results. the assembling is the work. the window only grows. it never shrinks on its own, so eventually something gets dropped. usually from the middle. usually without telling you. then the turn ends and the window is thrown away. not archived, thrown away. the next turn opens empty again and you re-explain what you already explained. good context engineering is knowing what to leave out, not what to pack in. the unit of work is one window. > graph engineering is the structure the same material arrives from the same sources. instead of packing it into a window, you pull entities out of it, resolve the duplicates into one node, and write typed edges between them. nothing here is stored as text you hope to find again. it is stored as a thing with a name and its connections to other things. when the turn ends, the graph is still there. the next turn does not start from zero. it starts by querying what already exists, and the query walks edges instead of guessing at similarity. good graph engineering is deciding what counts as the same thing twice. the unit of work is one relationship. > they are not alternatives the graph is what refills the window. context engineering decides what fits. graph engineering decides what there is to choose from. remove the graph and every session starts blind. remove the context work and the best structure in the world arrives as an unreadable dump. that also tells you which one broke. the answer drifted from what you actually said, or forgot something from this same session. that is the window. the answer is coherent but invents a connection that does not exist, or cannot join two facts it has clearly seen. that is the structure. people debug the prompt because the prompt is the easiest thing to edit. it keeps taking the blame for failures that live a layer down. save this - then read the full breakdown below

Hanako

19,160 views • 1 month ago

Michael Saylor gave me a two-word model for Bitcoin that explains this entire year: "Risk capital squared." Whatever is good for risk assets is really good for #Bitcoin. Whatever is bad for risk is really bad for it. Simple. But then he walked me through what's actually happening inside the risk markets right now, and this is the part most people miss. The equity market has split in two. A handful of AI monopolies raising $80 billion each, and then everyone else. "Every banker on Wall Street is out marketing the SpaceX deal, marketing the Anthropic deal." And the rotation logic is almost mechanical: "I know I'm going to get a 10 or 20% pop because the bankers are going to have to make the deal pop... So I'm going to get in on the deal and then I'll get out again." Mature money rolling out of three-year-old positions into hot deals engineered to pop. That's the drain. Bitcoin isn't funding the AI boom, it's maybe 2 or 3% of the capital, but it's the asset that feels every dollar leaving. And the line that reframes the whole "institutions abandoned Bitcoin" panic: "They're not maximalist in anything. They're not religious ideologues. They can be excited about this deal this week. They'll be excited about another asset next week. And they're all looking at relative value." The money didn't convert to another religion. It never had one. It chases relative value, and right now the deals pop harder. Which means the question isn't whether the capital comes back. It's what relative value looks like when the deals stop popping.

Michaël van de Poppe

32,100 views • 9 days ago

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 views • 2 months ago

.Haseeb >|< on why beating North Korea's hackers now comes down to who can afford the most compute: "If the attackers are spending 500 bucks, and good guys are spending maybe a couple bucks, they're like, oh, I just wanna check and see if there's any low-hanging fruit. Otherwise I'm not gonna use this code if I can tell it's obviously busted. So they spend a couple dollars worth of compute, and they say, ah, it looks good enough. It's a well-funded project. It's open source. Yeah, I guess I'll use it." "But North Korea is spending 500 bucks, 1,000 bucks, 2,000 bucks just grinding and grinding and grinding, running multiple agents overnight trying to find an attack." "That means that no matter how many good people are spending $2 worth of GLM compute on you, they will never be able to find the depth of the tree that North Korea is searching for. None of the normal users will ever get there, which means it's only really on the company." "The company is the only party that can coordinate enough compute to be able to actually expand the search space enough to out search an attacker." "But it does mean that attackers have the same problem. So if North Korea and Russia and China are all trying to hack your protocol, but they all spend, let's say, $5,000 each, if you spend $10,000 you will actually find everything that they can find and more." "Which in that sense, it benefits defenders over attackers in the long run, because the company can actually outspend any individual attacker in principle, and that used to be not sufficient defense in the old model because of the fact that attackers were uncorrelated with each other." Dragonfly >|<

MTS

35,600 views • 27 days ago