vraspar (@vraspar) header
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Does ML Compiler stuff | Working on @trytenjin

Joined November 2023
Photo by vraspar (@vraspar)Photo by vraspar (@vraspar)Photo by vraspar (@vraspar)Photo by vraspar (@vraspar)Photo by vraspar (@vraspar)
semantic web is going to be so back
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Anyone in SF doing Fashion tech ?
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vraspar retweeted
At a networking event busy being antisocial w the only other Waterloo founder @vraspar
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My dot disappeared from codex app What do ?
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I think the best way to use x402 is probably when you don’t have to think about it. Before, all my x402 usage was basically me testing or remembering to use it. With Tenjin router, I’ve been making about 15–20 x402 transactions per day without even realizing it. I have also noticed that it improved the quality of my agent’s work. I think strong harness integration is probably the way to go to increase x402 adoption. This also made me really bullish on x402 and how much better it is than getting API keys for every service.
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Jev prob has so many good military applications
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My biggest problem with x402 endpoints was that I had to remind my agent to use it and browse for it. This router helps a lot with that. It automatically picks x402 endpoints based the on the complexity of task and price :)
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It currently has Exa, Wolfram Alpha, firecrawl and few other curated endpoints. Will add more endpoints soon :)
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I really want to see charts showing Fable/Opus on high or xhigh reasoning. Most people don’t run Fable on Max. I want to know how models perform relative to Fable on high reasoning
Meta has released Muse Spark 1.3, their fourth Muse Spark model release in five months. Muse Spark 1.3 (max), which is in limited preview for Meta’s partners, scores 62 on the Artificial Analysis Intelligence Index, behind only Claude Fable 5.1 and Claude Opus 5. The variant available now, Muse Spark 1.3 (xhigh), scores 61 and ties with GPT-5.6 Sol (max) and Grok 4.6 (high). Both variants’ gains come primarily from improvements in agentic work and scientific capabilities Muse Spark 1.3 (xhigh) enters the Artificial Analysis Intelligence Index at 61, up 4 points from Muse Spark 1.2 (57, August) and 8 points from Muse Spark 1.1 (53, July). It enters tied with GPT-5.6 Sol (max), Grok 4.6 (high), and Claude Opus 5 (high), and behind Claude Fable 5.1 (max, 66), Claude Opus 5 (max, 63), and Claude Fable 5 (max, 62) Muse Spark 1.3 (max), which is in a limited preview stage, lands at 62. This higher index score is enabled by gains vs. Muse Spark 1.3 (xhigh) in Tau3-Bench Banking (52% vs. 47%) and GDPval-AA v2 (1,754 Elo vs. 1,709). Muse Spark 1.3 (max) is second only to Claude’s Fable and Opus variants in total score Congratulations to @AIatMeta, @finkd, and @alexandr_wang on the release! Key Takeaways: ➤ Continued improvement on agentic knowledge work tasks. At the launch of Muse Spark 1.2, we noted its significant gains in agentic knowledge work performance vs. Muse Spark 1.1. The latest iteration continues this trend, with Muse Spark 1.3 (xhigh) demonstrating a notable 12-point gain vs. Muse Spark 1.2 in Tau3-Bench Banking (35% to 47%), a 5-point gain in Terminal-Bench 2.1 (80% to 85%), and a new GDPval-AA v2 Elo of 1709 against its predecessor’s 1615. Muse Spark 1.3 (max) improves further on Tau3-Bench Banking (52%) and GDPval-AA v2 (1,754 Elo). This Tau3-Bench Banking score is #1 among all models. Muse Spark 1.3 (max) achieves these higher agentic work scores by using more turns and total reasoning tokens, reasoning 62% more on GDPval-AA v2 and 28% more on Tau3-Bench Banking compared to Muse Spark 1.3 (xhigh) ➤ The lowest cost per task for any model at 59+ on the Artificial Analysis Intelligence Index. Muse Spark 1.3 (xhigh) costs $0.55 per Intelligence Index task at Meta's unchanged $1.25/$4.25 per 1M token pricing ($0.15 for cached input), with its peers GPT-5.6 Sol (max) and Grok 4.6 (high) costing $0.95 and $0.94 respectively, a 70%+ premium. This places Muse Spark 1.3 (xhigh) on the Pareto frontier for Intelligence vs. Cost per Task. Its cost per task is higher than Muse Spark 1.2 ($0.40 per task), driven by ~57% more input tokens per task on agentic evaluations, with output tokens up only ~8%. Pricing for Muse Spark 1.3 (max) is not yet publicly available ➤ Scientific Reasoning results rose across the board, led by CritPt. CritPt was the standout non-agentic score gain vs. Muse Spark 1.2, with a material +8 points for the xhigh variant (18% to 26%), and GPQA Diamond achieved +4 points (90% to 94%), while Humanity’s Last Exam and SciCode each gained a more modest 2-3 points (45% to 47% and 56% to 59%, respectively). Muse Spark 1.3 (max) achieved roughly similar scores to the xhigh variant, gaining 2 points in Humanity’s Last Exam, tying on GPQA Diamond, and losing a point on CritPt vs. Muse Spark 1.3 (xhigh) ➤ Minor regressions in only two evaluations. Both Muse Spark 1.3 (xhigh) and Muse Spark 1.3 (max) dropped 4 points in AA-LCR (83% to 79%) when compared to Muse Spark 1.2, and AA-Omniscience (Accuracy) fell 3 points for xhigh and 1 point for max. The drops in AA-Omniscience (Accuracy) are due to a higher abstention rate (not answering questions when unsure), which also lowered the hallucination rate for Muse Spark 1.3 (xhigh) Other model details (xhigh variant): ➤ Context window: 1M tokens, unchanged from Muse Spark 1.2 ➤ Pricing: unchanged from Muse Spark 1.2: $1.25/$4.25 per 1M input/output tokens, with cache hits discounted to $0.15 per 1M ➤ Input modalities: text, image, video ➤ Availability: Meta's first-party API and Muse Code
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Is it just me or Fable 5.1 feels worse than Fable 5 ?
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I need random Claude code/Codex session transcripts for experiment I am doing. Of course I would pay, if anyone wants to sell their Claude code/Codex session transcripts
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vraspar retweeted
Enabled by x402. It is also especially powerful for x402 developers and users. Each piece your agent buys can save you real money and you can produce much better and valuable pieces with it.
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I came across describe.net and made me realize that for ERC 8004 to be useful irl we also need to create standard for trust layer (reputation protocol). Maybe some modifications of EigenTrust algorithm. Would love to know if anyone is working on it.
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Thanks! @trytenjin has infra that makes creating x402 endpoint super easy with rate limiter, telemetry and few other nice features. We can usually spin up any endpoint with just one prompt. Can also use Tenjin to monetize any content for agents easily
x402 endpoint for searching names from phone numbers 🙌🙌🙌 im horrible at saving numbers this has already clutched up for me the pace of software development is purely limited to the ideation process
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You can learn a lot about someone from how they talk to their agents
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TIL Plain alternating turns have a hidden asymmetry. In ABAB..., A always gets the first pick inside each 2 step window. Thue-Morse fixes this in a nice way: ABBA BAAB... You alternate not just who picks but who also gets to be first.
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woke up and the memo is still mispriced
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