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Aptos Labs built the parallel execution design that other chains went on to adopt Running a block one transaction at a time does not scale. One slow transaction stalls everything queued behind it, and throughput collapses under load. One approach asks developers to list in advance every piece of...

19,231 Aufrufe • vor 5 Tagen •via X (Twitter)

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Somewhere around sixty you get handed a new set of instructions. Lift lighter. Keep the reps high. Do not tax yourself too much. Put the saved effort into cardio. It is the exact reverse of what an ageing body needs, and the people handing it out have the mechanism sitting right in front of them. Recovery gets worse with age. Nobody argues with that. The older body clears fatigue more slowly, repairs more slowly, and tolerates far less accumulated work before progress stops entirely. Every GP, every physio, every trainer will nod along to that sentence. Then watch what gets prescribed on the back of it. High reps. Long burning sets. Circuits. Three sessions of cardio stacked on top. A protocol whose main product is fatigue, given to the person with the least capacity left to absorb any. They identified a recovery problem and prescribed more recovery cost. The answer runs the other way and it is not complicated. If your recovery budget has shrunk, you spend it on whatever returns the most growth per unit of fatigue, and that is a heavy set of five. Four to six reps, a handful of lifts, three minutes between sets, done inside the hour. Nearly every rep is a growth rep. Almost nothing goes on the burning, the sweating and the gasping, which build nothing at all and then bill you for four days. Twenty-five reps taken to failure is a fortnight of fatigue for a fraction of the stimulus. That is not the cautious option for a sixty-five-year-old. It is the most reckless thing on the timetable. Now the part that actually matters. Ageing is not one process. It is a list. Muscle wastes. Bone thins. Tendon softens. The fast fibres that catch you when the pavement arrives early vanish first while the slow ones sit there in perfect health. Motor units drop out. The nervous system stops asking for full effort because nothing has demanded full effort in fifteen years. Read that list back and tell me what heavy resistance training does. It builds muscle. It loads bone, which is the only language bone speaks. It stiffens tendon. It recruits the fast fibres, because that is what heavy means physiologically and there is no other route in. It forces the nervous system to ask for everything again. Every item on the list of what ageing takes is on the list of what a heavy set gives back. Nothing else on earth does that. Not a walk, not a class, not a pill, not twenty minutes on a machine with the paper open. You were told to go gently because somebody quietly decided you were finishing. Go heavy, because you are not.

Sama Hoole

16,526 Aufrufe • vor 1 Monat

Day 11/90 of Inference Engineering How does vLLM work and how is it used in production? Before we discuss how vLLM works internally, it helps to understand what vLLM is. At a high level, vLLM is an inference engine that is designed to serve LLMs to thousands of concurrent users efficiently while managing scarce compute and memory. The goal for vLLM is to maximize throughput and minimize latency; optimizing for the best inference economics and experience for end users. With every request from the end user, it eventually ends up in the engine core, gets scheduled alongside other requests from other concurrent users, executes on the GPU, and updates the KV cache with the new key and value vectors, and streams the tokens back to the user. The Scheduler decides what requests should execute next while continuously batching requests together to maximize GPU utilization. Continuous batching is an inference optimization that allows new requests to join a running batch as other requests finish generating tokens. This helps with keeping the GPU utilization high instead of letting it sit idle waiting for an entire batch to complete generating. After the scheduler dispatches the selected batch to the Model Executor, the Model Executor prepares the tensors and metadata required for inference, retrieves each request’s block table from KV Cache Manager, launches the optimized transformer forward pass on the GPU, computes the logits, updates the KV cache with the new key and value vectors, and finally returns the results for sampling and streaming. The KV Cache Manager uses the PagedAttention memory layout to allocate fixed-size cache blocks on demand and maintains a Free Block Queue on the CPU that tracks which blocks in the GPU’s Paged KV Cache are currently free. When a request needs additional KV cache space, the KV Cache manager takes a free block from the queue and assigns it to that request, thus avoiding an expensive search through GPU memory for available cache blocks. All of these components form the core of vLLM’s inference engine. The Scheduler determines what requests are executed, the Model Executor determines how those requests are executed, the KV Cache Manager determines where each request’s KV cache lives using the PagedAttention Memory Layout. This architecture enables vLLM to serve thousands of concurrent requests with high throughput, low latency, and efficient GPU memory utilization. Heres a little animation that visualizes everything! - I've also completed the forward pass for my mnist.c project. I had a nice chat with shrey birmiwal, such a knowledgeable guy. Excited to learn more about vLLM and implement a tiny-vLLM one day.

max fu

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Here's an excellent video from Florian (follow) that shows charged water on the left and neutral water in the right. This is a visual confirmation of two known effects; Electrostatic induction and dielectric relaxation time. Electrostatic induction is like when a hair-rubbed balloon sticks to the wall, despite the wall being neutral; the balloon causes charges in the wall to polarize so the wall side near the balloon turns negative and the other side of the wall is positive. That's electrostatic induction and that's why you have raindrops that stick to a car window for hours of wind and gravity, rain has a small negative charge. Wouldn't work with tap water. Now, if you remove the balloon, the wall doesn't return to a neutral state immediately. It'd happen quickly if we had a balloon on the other side of the wall, but by itself this can take seconds, minutes, hours or even days, depending on the material and thickness. Glass is quite slow, and you can see the electric forces holding each other will hold off gravity for some time; and that the water greatly prefers not to be broken off, water is like an uncountable number of those walls in series, and they all cling to each other in proportion to how many balloons we have, ie how many excess electrons we have. Furthermore, when the water is extremely charged, it will not even leave the thin remnant layer of water that trails behind, as the cohesive internal forces outweigh those by induced to outside neutral surfaces.

N'Golo⚡Wizard.Talk

22,198 Aufrufe • vor 2 Monaten

Every man lives two lives. The second begins when he realises that he can max out hypertrophy in 15 second sets. Four or five reps build muscle. The last four or five, where the bar slows on its own whether you like it or not. That is where the high-threshold motor units finally get called up. Where the cross-bridges are attached in maximum number at once. Where the fibre takes the tension it has been waiting all set for. Mechanical tension is the only language the muscle reads. It does not have a second one. Everything either side of that is filler, and somebody is paid to sell you filler. Tempo is filler. You are 30 to 50 per cent stronger lowering a weight than lifting it, so no descent on earth is slow enough to threaten you. Drag it out and all you have done is put a lighter weight on the half that counted. The pump is filler. Lactate infused straight into the blood grows nothing. Blood flow restricted on a resting muscle grows nothing. Swelling is a feeling and it gets no vote. The mind-muscle connection is filler. You cannot instruct a muscle in which fibres to use. You tell it to contract. The load decides the rest. So: a weight where the fourth rep slows by itself. Drive it. Stop one short. Rack it. Fifteen seconds. That set has already delivered everything your three sets of twelve spent forty minutes promising. Nobody has ever built a business on fifteen seconds of hard work, so they built one on everything you could bolt onto it.

Sama Hoole

17,051 Aufrufe • vor 1 Monat

**Traditional Engine Order Telegraphs:** The bridge unit and the engine-room unit are connected by two chains (or wire cables) that run through conduits over sprockets, forming a continuous loop. When the bridge officer moves the handle, it turns a sprocket that pulls the chain, which in turn turns the corresponding sprocket and pointer in the engine room. This classic system does not require electricity; it relies on a direct physical linkage, ensuring that both dials always mirror each other. Here’s a typical sequence of operations: The bridge officer grabs the handle and swings it to the command position, for example, "Half Ahead." This movement not only moves the engine-room pointer but also rings a bell or gong to alert the engineers. The engineers then adjust their own handle to match the position indicated by the bridge officer; this movement serves as an acknowledgment and rings the bell back on the bridge, confirming that the order has been received and understood. Only after this acknowledgment do the engineers work the throttle and reversing gear to carry out the order. It is common practice to first execute a full sweep of the handle, moving it all the way to one extreme and back before settling on the actual command position. This exaggerated motion serves two purposes: it rings the gong loudly and unmistakably, ensuring everyone is aware that a new order is coming, and it checks that the mechanism and chains are not stuck or slack. On some ships, this full sweep also signifies an important or urgent change (such as switching from ahead to astern), emphasizing to the crew that they need to pay attention because the situation is critical. After completing this sweep, the handle is adjusted to the desired position. This telegraph is from the Steamship Shieldhall (video credit as well). Go check her out on

Bart 🌊⚓️

107,644 Aufrufe • vor 1 Monat

I’m probably one of the only Teslanaires out there, if not one of the very few, still cutting my own hair. I cut my own hair again today, and it reminded me that becoming a multi-millionaire usually isn’t a random coincidence. People see the $ and think it just happened. What they usually don’t see are the small habits behind it. Of course, I could go spend $25–$50 on a haircut that probably looks better than the one I give myself. But that’s not really what matters to me. I don’t care that much about looking perfect. I care about controlling my time. I care about staying grounded. I care about keeping the kind of habits that helped me build wealth in the first place. And honestly, I enjoy doing it. I’ve been cutting my own hair for so many years that I don’t even think about going to the barber anymore. It’s just normal to me now. It saves time, keeps me frugal, and reminds me that wealth is usually built in the small choices nobody claps for. That’s the part people miss. A lot of people see wealth and assume it was luck. But a lot of the time, it’s really the result of small disciplined habits repeated for years. Not wasting $ just bc you can. Not wasting time just bc other people do. And the funny part is, one day my fleet of Tesla Bots will probably be doing it for me anyway. But until then, I’m good doing it myself. Bc to me, being wealthy was never about trying to look rich. It was about building a mindset. A mindset that values time, discipline, and freedom more than appearances. And once you really live that way, it shows up in a lot of things, even something as simple as cutting your own hair.

Teslaconomics

16,514 Aufrufe • vor 5 Monaten

Talking with someone the other day that estimated they had about 25,000 idle connections to Postgres. My actual response to them: "holy shit". They double checked, it was only about 12,000 Same day had a conversation with someone saying they didn't need pgbouncer because of activerecord's connection pooling. Let's dig into connection pooling in Postgres. Prior to Postgres 14 every connection to the database consumed memory, roughly 10MB, it may be slightly less but it still wasn't free. Even beyond Postgres 14 there is still various contention that happens when Postgres starts to use a connection. An application pooler maintains a set of connections and hands them out when needed on the application side. These are idle and real connections against the database that indeed do impact performance negatively. In contrast pgbouncer speaks the wire protocol, waits for the begin part of the transaction and then uses a connection. It more strictly manages how many idle ones it's having instead of per web server you're running. pgbouncer up until recently really needed to be run in transaction mode (which meant disabling prepared statements in your application framework). prepared_statement support in pgbouncer was added recently, and now you don't have to disable. Even when running with an older version of pgbouncer with prepared_statements disabled you'd still see a big performance gain. A quick check to know if you'd benefit from pgbouncer, run this query: SELECT count(*), state FROM pg_stat_activity GROUP BY 2; If you're idle account is high (yes this is dependent on your view, but to me if it's above 25-30 range, and especially if active is until half that) then you'd already start to benefit from pgbouncer. If it's at 10,000 then post haste get pgbouncer in place. Finally, you don't have to not use a framework pooler, they're fine, but don't think it replaces a native Postgres connection pooler.

Craig Kerstiens

12,411 Aufrufe • vor 1 Jahr

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 Aufrufe • vor 21 Tagen