Prezzo di GitcoinGTC

product review: @OctantApp x @HumnPassport sybil resistance for epoch 8 I've been maintaining 3-5 wallets with GLM in it ever since octant moved to quadratic voting so i can test out their defences each epoch (i vote for separate projects with each wallet so it doesn't affect the allocation) So what were the results? Mostly i was just astounded at the horrible UX users have to go through I don't expect octant to hit a significant scale as long as they make the user jump through so many hoops, most of which are sadly not even needed ☹️ the data Wallet 1: cleared the sybil score since i was an eth enthusiast with a lot of onchain activity However this did not reflect automatically on octants dashboard. for some reason i had to go to human passport and manually verify this stamp before it got updated on octants page? to what end, since its literally just checking my onchain activity and should show up automatically in octant without having to go to a separate url? Wallet 2: i was on the octant guest list as a long time glm locker so it cleared but again i had to visit the passport page and verify this before it showed in the octant app? how many users are not taking this superfluous step so their matching doesn't get counted? they really need to make these 2 stamps (onchain and guest list) automatic on the octant front end rather than having the user needlessly connect their wallet to passport Wallet 3: flagged as a sybil by Octant despite passing the score due to Eth enthusiast. well done! Wallet 4: this one of mine actually got hacked a week ago and all the GLM in it withdrawn 🙈 Octant flagged it as a sybil too i think the compromise happened when i was figuring out how to bridge to unichain for testing the butter app thankfully didn't get rekt as i follow the MO of keeping wallets storing money separate from those that interact with apps 😮‍💨 Wallet 5: Ah, this was the root of my frustration i needed to reach a score of 15, so here's what i did; - NFT ownership : 1 point - phone number verification: 1.5 points - discord + google = 1 point linkedin with 1.5 points did not work much to my frustration. kept getting the below error - facial scan: 5 points kudos to Silk on how smooth the process was - gitcoin staking: 6.3 points i should have been over the limit of 15 but guess what? octants connection with human passport is broken & it literally showed me different scores from their app vs the human passport page 🤦 I'd need the octant team to confirm but i suspect its because they don't accept GTC staking but the passport team missed this memo & still includes it on the octant stamp page i wasted $50 and a ton of time for no reason. my other options for getting above the threshold; - identity verification: did not work for my indian passport, much to my frustration and repeated attempts (its 16 points so would have cleared the threshold here itself) i also don't trust centralized exchanges with my data so couldn't avail the binance stamp (10 points) or base (4 points) so i gave up on wallet 5 which is maybe good? Overall 2 wallets had a passing score, i think with enough time i could have got wallet 5 above the threshold too Conclusion Steve jobs famously said that design is not how something looks, its how it works and sybil resistance measures don't currently work in octant, at least not without imposing a ton of user friction Here are 3 low hanging fixes; 1. wallets that are on the guest list or have adequate onchain activity should automatically be getting the full matching without the user even knowing about any sybil stuff currently they have to go to the passport page from the octant app, click on "verify stamp" under ethereum activity, go back to octant, click on "recalculate" and only then make their allocations. which is a lot of extra steps for no benefit 2. if a wallet is not verified, it only gets 10% of the impact for matching fund allocations imo it should be a sliding scale rather than a binary (either 10% or 100%), where the more reliable your wallet the more matching it counts for that way its up to the user on whether they want to do more work to increase their match rather than something that must be done to reach the better of the 2 options 3. have more diversity, why are orbs not accepted? or self protocol? not sure how much i agree with octants approach of tying themselves to a particular provider for eg i should have been able to use @AnonAadhaar to verify my government identity but because india wasn't accepted by the team doing sybil for octant i couldn't use my government id at all i also would have liked credit card checking to count for some points as always i love the octant team and please allocate in epoch 8 if you haven't already what's becoming clear is that if octant wants to reach the next level of scale they need to radically rethink sybil protection

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我说下昨天为啥卖$nihao吧。 晚上睡不着 我看评论区有人提徐乐。 我也不认识。 然后去网上搜。 搜完又在推特搜。 又搜gtc. 又搜了

Google DeepMind on Tuesday introduced a new language model called Gemini Robotics On-Device. The firm revealed that the model can run tasks locally on robots without an internet connection. The new model, which builds on the company’s previous Gemini Robotics AI model that was released in March, can control a robot’s movements. Google also acknowledged that the vision-language-action model (VLA) is small and efficient enough to run directly on a robot. According to the company, developers can control and fine-tune the model to suit various needs using natural language prompts. Head of robotics at Google DeepMind, Carolina Parada, maintained that the original Gemini Robotics model uses a hybrid approach, allowing it to operate on-device and on the cloud. She said that with the new device-only model, users can access offline features almost as well as the flagship’s. The tech company claims that the model performs at a level close to the cloud-based Gemini Robotics model in benchmarks. Google also said it outperforms others on-device modes in general benchmarks, though it didn’t name those models. “The Gemini Robotics hybrid model is still more powerful, but we’re actually quite surprised at how strong this on-device model is. I would think about it as a starter model or as a model for applications that just have poor connectivity.” -Carolina Parada, Head of Robotics at Google DeepMind. The firm illustrated in the demo robots running the local model, unzipping bags, and folding clothes. Google acknowledged that while the model was trained for ALOHA robots, it later adapted it to work on a bi-arm Franka FR3 robot and the Apollo humanoid robot by Apptronik. The tech company claims the bi-arm Franka FR3 was successful in tackling scenarios and objects it hadn’t seen before, like doing assembly on an industrial belt. The firm mentioned that developers can show robots 50 to 100 demonstrations of tasks to train them on new tasks using the models on the MuJoCo physics simulator. Google DeepMind also mentioned the release of a software development kit called the Gemini Robotics SDK. The company revealed that its Robotics SDK provides full lifecycle tooling necessary for using Gemini Robotics models, including accessing checkpoints, serving a model, evaluating the model on the robot and in the sim, uploading data, and fine-tuning it. The firm disclosed that its on-device Gemini Robotics model and its SDK will be available to a group of trusted testers while Google continues to work toward minimizing safety risks. Other companies that use AI models are also showing interest in robotics. Nvidia is building a platform to create foundational models for humanoids. The firm’s CEO, Jensen Huang, noted that building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today. Huang argued that the humanoid factor is one of the most contested topics in the world of robotics at the moment. He acknowledged that it’s raising venture capital by the boatload while generating massive skepticism along the way. Nvidia has also been championing robotic innovation through initiatives like Isaac and Jetson. Last year in March, at its annual GTC developer conference, the company joined the humanoid race with Project GROOT. Nvidia referred to the new platform as a general-purpose foundation model for humanoid robots. The firm said GROOT will support new hardware from Nvidia as well. Hugging Face is not only developing open models and data sets for robotics, but it is also working on robots. The firm revealed earlier this month an OpenAI model for robotics called SmolVLA. The company claims the model is trained on community-shared datasets and outperforms much larger models for robotics in both virtual and real-world environments. Hugging Face also revealed that SmolVLA aims to democratize access to vision-language-action (VLA) models and accelerate research toward generalist robotic agents. Last year, the firm launched LeRobot, a collection of robotics-focused models, datasets, and tools. More recently, Hugging Face acquired Pollen Robotics, a robotics startup based in France, and revealed several inexpensive robotics systems, including humanoids, for purchase. KEY Difference Wire helps crypto brands break through and dominate headlines fast

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