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The Digital Embrace | Social Security’s Final Paper Cutoff The digital embrace Social Security shuts the ...

Sequoyah’s syllabary faced suspicion initially, but after a demonstration, his version of “talking leaves” was widely embraced. And then the word spread

Hey everyone! I teach CS and programming at a small school in Syria and I'm in the middle of designing a full 5-year hardware-focused IT curriculum. I'd love some honest feedback from people with hands-on robotics/embedded systems experience. Here's the current plan: - **Grade 7:** Lego Spike Prime + Micro:bit - **Grade 8:** Arduino Uno with multiple sensors - **Grade 9:** Project-based learning with Arduino *(see note below)* - **Grade 10:** ESP32 - **Grade 11:** Advanced ESP32 + Raspberry Pi **Note on Grade 9:** This is the Basic Education Certificate year (think national standardized exams), so the curriculum here is intentionally lighter — more of a consolidation year with small projects rather than introducing heavy new concepts. Students won't have the bandwidth for anything too demanding, so I'm keeping it Arduino-based but project-driven to keep them engaged without piling on. --- **My questions for the community:** **Is this hardware progression age-appropriate?** Students range from roughly 12–17. Does the jump between stages feel right, or are there places where it's too much too soon (or not enough)? **ESP32 in grades 10–11 — good idea or not?** I like it because it covers WiFi/BLE, has plenty of GPIO, and feels like a natural step up from Arduino. But I've heard mixed things about its learning curve and toolchain complexity for high schoolers. What's been your experience? **Are there better alternatives to the ESP32 at that level?** I'm open to suggestions — whether that's staying on the Arduino ecosystem (Nano 33 IoT, Portenta, Uno R4 ?), or something else entirely. Budget is a consideration but not the only one. Any feedback appreciated — curriculum design resources, pitfalls to avoid, or even just "this worked great for my students" stories. Thanks in advance! submitted by /u/Pastalini_Byte [link] [Kommentare]

https://preview.redd.it/w70oeo7mhi6h1.png?width=1875&format=png&auto=webp&s=5d55785b820d0396ae7bbc6d1a76ee7ee410f51a On this day in the last cycle we went lower, just saying. submitted by /u/Own_Chapter9338 [link] [Kommentare]

Full disclosure: this is directional, not a paper. n=120 tasks, one internal evaluator, not peer reviewed. I work at an LLM infrastructure company. This experiment was done on my own time and is not a company claim. Karpathy's framework classifies tasks by verifiability. Can output be mechanically checked? High verifiability tasks like code compilation and structured JSON extraction are safer because the verifier catches errors. Low verifiability tasks like creative writing are riskier. I wondered if high verifiability tasks are also easier in practice. Can a weaker model do them as well as a frontier model if the verifier catches mistakes? Setup was 120 tasks across four categories. Code unit tests, structured extraction, multi hop reasoning, creative summarization. Three models: Claude Sonnet 4.6, GPT 5.5, local Mistral 3 8B via vLLM 0.6.3. Pass rate for the first two, human rating 1 to 5 for the last two. Results were messy. Code unit tests: Sonnet 4.6 94%, GPT 5.5 91%, Mistral 3 8B 87%. With one retry Mistral 3 hit 95%. That surprised me. I expected the gap to be bigger. Structured extraction: Sonnet 4.6 97%, GPT 5.5 94%, Mistral 3 8B 89%. With retry 96%. Also closer than I expected. But here is where it got weird. Sonnet 4.6 initially scored worse than GPT 5.5 on structured extraction, which made no sense. Turns out our JSON schema had an ambiguous nested array that confused Claude's tool use parser. Fixing the schema brought Sonnet to 98%, but I kept the original numbers in the table because the mistake is part of the story. Your verifier is only as good as your schema. Multi hop reasoning: Sonnet 4.6 78%, GPT 5.5 71%, Mistral 3 8B 51%. Retry didn't help. The model would hallucinate reasoning paths consistently. This is where the capability gap was real. Creative summarization: Sonnet 4.6 4.2 out of 5, GPT 5.5 3.9 out of 5, Mistral 3 8B 3.1 out of 5. Expected. Interpretation: high verifiability tasks seem simpler in the sense that weaker model plus verifier can approach frontier performance. Low verifiability tasks show the expected gap. Limitations: n=120 is tiny. Need 10x for confidence. Our verifier is just JSON Schema plus regexes. Constrained decoding might change the calculus entirely. I also didn't control for prompt length well. Any prompt over 8k tokens was excluded because Mistral 3 8B degrades near its limit, which probably skewed the sample. submitted by /u/DragonfruitAlone4497 [link] [Kommentare]

Hello everyone, I'm 20 years old and have a huge passion for finance. I'm currently one semester away from graduating college and want to be a financial advisor. I'm a huge ben Felix fan and SCV/Value believer and have a system that I stick too every week. With that being said, I hit an absolute homerun on crypto last year. Back when I was 15 I also used to mine BTC on a nice hash account off my gaming computer that didn't last long because my parents thought it was all fake and was raising our electricity bill. Where I'm going with this is that I have somewhat deep roots with crypto as a whole and told myself I wouldn't touch it again after I did so well at such a young age because I know most people don't make it out of this space alive. Recently however, I realized I'm in a very blessed spot and have this urge to get back into it. I would keep it very plain and simple and just own a single bitcoin and ignore it for the next few years. With bitcoin's current price my overall portfolio would be around 38% BTC which I'm not opposed to but it is definitely a mindset change. I'm partially used to it by now considering I survived liberation day 2025. Just looking to hear what people have to say about my situation, any feedback is much appreciated. submitted by /u/Overkill-621 [link] [Kommentare]

I looked at my portfolio today and honestly I’m not even sure what I’m doing anymore. I’ve got BTC, ETH, a couple of L1s, some random small caps I bought during hype, and a few “conviction plays” I can’t even explain anymore. It feels like I tried to reduce risk by diversifying… but ended up just spreading confusion across 12 coins. How do you actually know when your portfolio is “too scattered” vs reasonably diversified? submitted by /u/Sensitive_Income6998 [link] [Kommentare]

Hi everyone, I recently completed my Bachelor's degree in Mechatronics and Robotics Engineering, and I'm currently focusing on improving my ROS 2 skills. I'm looking for individuals, teams, or communities that are actively working with ROS 2 and would be open to having a beginner- or intermediate-level member join them. My goal is to gain practical experience, contribute to projects, learn best practices, and develop my robotics software skills. If you know of any ROS 2 groups, open-source projects, Discord servers, study groups, or communities where I can learn and collaborate, I would greatly appreciate your recommendations. I'm motivated to learn, willing to put in the work, and eager to contribute wherever I can. Thank you in advance. submitted by /u/Maleficent_Youth_168 [link] [Kommentare]

Here you can see how Bill Gates thought about memory: In this Software Notes newsletter from 1975, he explains the strategies and tricks he employs to squeeze things to the utmost. Back then, "memory budgeting" wasn't just a best practice, it was a survival skill. What are your approaches today? You can see more how Gates approaches systems design, handwritten diagrams, and BASIC source code here : https://www.programmersatwork.net/bill-gates submitted by /u/slammers00 [link] [Kommentare]

I'm hoping some of the Bitcoin experts here can help me understand what happened. ​ I recently made a BTC withdrawal from GoMining to my Kraken deposit address. ​ GoMining withdrawal screen showed: ​ Withdrawal Amount: 0.00343202 BTC ​ Fee: 0 BTC ​ You'll Receive: 0.00343202 BTC ​ Kraken later credited: ​ 0.00342000 BTC ​ Difference: ​ 0.00001202 BTC (1,202 sats) ​ Kraken manually reviewed the deposit and confirmed that transaction: ​ a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ was the transaction that delivered funds to my Kraken deposit address and that the amount received was exactly: ​ 0.00342000 BTC ​ Looking at the transaction on mempool.space: ​ Transaction: a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ Input: ​ bc1qr2hhvpf3m0z4sx90nxqydccxlxkf4pe5mjfhcr ​ 0.00343202 BTC ​ Outputs: ​ 0.00342000 BTC to: 3KtixuucHp3ZFSex3847Liv6v8m1xnvt8y ​ 0.00001060 BTC to: bc1qgcq078znysgm6fsveu894e32mrzqy9n9ncjg0w ​ Miner Fee: ​ 0.00000142 BTC (142 sats) ​ The math balances perfectly: ​ 0.00342000 BTC + 0.00001060 BTC + 0.00000142 BTC = 0.00343202 BTC ​ GoMining states that their withdrawal transaction is: ​ 95333b087645e5fa1c2b64ea5e62f41ac3929a673aa7186697cf3a73916cd51d ​ and that transaction a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 was not created by them. ​ Kraken states that they do not know who controls: ​ bc1qr2hhvpf3m0z4sx90nxqydccxlxkf4pe5mjfhcr ​ and that GoMining would need to explain how transaction: ​ 95333b087645e5fa1c2b64ea5e62f41ac3929a673aa7186697cf3a73916cd51d ​ ultimately resulted in transaction: ​ a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ being created. ​ My goal is not to accuse either company of anything. ​ I'm simply trying to understand: ​ Who likely controlled the input address: bc1qr2hhvpf3m0z4sx90nxqydccxlxkf4pe5mjfhcr ​ What the 1,060 sat output to: bc1qgcq078znysgm6fsveu894e32mrzqy9n9ncjg0w ​ most likely represents. ​ Whether this looks like a normal custody, batching, forwarding, exchange, or UTXO management process. ​ Whether anyone can determine the relationship between: ​ 95333b087645e5fa1c2b64ea5e62f41ac3929a673aa7186697cf3a73916cd51d ​ and ​ a86609911ead87031cf75a808a965802f3adaa9f07e4fd0b88d66ea72a159112 ​ from publicly available blockchain data. ​ Any help would be appreciated. submitted by /u/bbrian017 [link] [Kommentare]

Hey everyone, Daniel here, we’re building Vastnaut One, a 4x4 exoskeleton designed for people moving through demanding terrain with load where fatigue tends to build gradually across hips and knees, especially on descents. What you’re looking at here is part of our joint aging tests, repeating the same movement cycles than any normal hike would require. At some point, it stops looking like testing and starts looking a bit obsessive. Our system works across both hips and knees in real time step by step based on movement, terrain, and load. The goal isn’t to change how you move, but to redistribute effort over time so the later miles feel closer to the first. Curious how others here think about for a wearable like this, and what do you usually trust as a good enough cycle count. submitted by /u/dan1elfeng [link] [Kommentare]