Yi Z. (@yz) header
Yi Z. (@yz) profile picture

Curious ML/DL hacker. Love cats, daydreaming, chatting with AI, and pondering over all things logical. Meta; ex: Stakefish, Twitter, Google, CMU, Bowdoin.

San Francisco, CA
Joined September 2009
Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)Photo by Yi Z. (@yz)
Meta is back! Muse Spark scores 52 on the Artificial Analysis Intelligence Index, behind only Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Muse Spark is the first new release since Llama 4 in April 2025 and also Meta's first release that is not open weights Muse Spark is a new model from @Meta evaluated on Artificial Analysis. We were given early access by Meta to independently benchmark the model. It is the first frontier-class model from Meta since Llama 4 Maverick was released in April 2025, and notably the first @AIatMeta model that is not being released as open weights. The release follows Meta's reorganization of its AI efforts under Meta Superintelligence Labs, and signals that Meta is re-entering the frontier race after roughly a year of relative quiet. For context, Llama 4 Maverick and Scout scored 18 and 13 respectively on the Artificial Analysis Intelligence Index as non-reasoning models at the time of their release, while Muse Spark scores 52. Muse Spark essentially closes the gap between to the frontier in a single release. The model is not open source and is not yet accessible via an API but Meta has shared they expect this to come soon. Meta is also integrating Muse Spark into their first party products including their Meta AI chat product, Facebook, Instagram and Threads. Key takeaways from our benchmarks: ➤ Muse Spark scores 52 on the Artificial Analysis Intelligence Index, placing it within the top 5 models we have benchmarked. It sits ahead of Claude Sonnet 4.6, GLM-5.1, MiniMax-M2.7, Grok 4.20 and behind Gemini 3.1 Pro Preview, GPT-5.4 and Claude Opus 4.6 ➤ Muse Spark is notably token efficient for its intelligence level. It used 58M output tokens to run the Intelligence Index, comparable to Gemini 3.1 Pro Preview (57M) and notably lower than Claude Opus 4.6 (Adaptive Reasoning, max effort, 157M), GPT-5.4 (xhigh, 120M) and GLM-5 (110M) ➤ Muse Spark is the second-most capable vision model we have benchmarked. It scores 80.5% on MMMU-Pro, behind only Gemini 3.1 Pro Preview (82.4%) ➤ Muse Spark performs strongly on reasoning and instruction-following evaluations. It scores 39.9% on HLE, trailing only Gemini 3.1 Pro Preview (44.7%) and GPT-5.4 (xhigh, 41.6%). The model also achieved 5th highest in CritPT with a score of 11%, an eval that is focused on difficult physics research questions. This is substantially above above Gemini 3 Flash (9%) and Claude 4.6 Sonnet (3%) ➤ Agentic performance does not stand out. On GDPval-AA, our evalaution focused on real world work tasks, Muse Spark scores 1427, behind both Claude Sonnet 4.6 at 1648 and GPT-5.4 at 1676, but ahead of Gemini 3.1 Pro Preview at 1320. On On TerminalBench Hard, Muse Spark trails Claude Sonnet 4.6, GPT-5.4, and Gemini 3.1 Pro. Muse Spark joins others in achieving a high τ²-Bench Telecom score of 92% Key model details: ➤ Modalities: Multimodal including text and vision input, text output ➤ License: Proprietary, Meta's first frontier model not released as open weights ➤ Availability: No public API at the time of publishing. Meta expects to provide API access soon. Meta has started integration into their first party AI offering Meta AI and inside Facebook, Instagram, and Threads
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Let’s be clear: DeepSeek r1 isn’t about who “races faster”—it’s about the inherent flaw in closed models. The future of AI isn’t owned by those who hide code or stockpile chips. It’s built on trust, and trust requires transparency. When models are black boxes, you surrender control over data privacy, culturally aligned ethics, and post-training customization for real world scenarios. That’s not leadership—it’s liability. China’s pretraining consolidation proves a simple truth: AI is becoming a commodity. The real value lies not in the model but in what you do with it. Why waste billions reinventing closed-source base models when the market craves applications that solve poverty, climate crises, or healthcare gaps? Labs clinging to secrecy risk irrelevance—like doubling down on fax machines as email took over. Consider the irony: Today, free models come from quant firms while “nonprofits” charge premiums for access. OpenAI’s pivot from “open” to walled gardens betrays the very ethos that birthed modern AI. Meanwhile, market economy principles prevail: restrict access, and replacements emerge. The lesson? Trust beats control. Open models engage developers. Closed models breed suspicion—and suspicion fuels replacement. How to “maintain leads”? Stop gatekeeping. Build open infrastructure the world trusts, like the internet. History doesn’t reward those clinging to scarcity. It rewards those who empower the many. The choice is yours.
DeepSeek is a wake up call for America, but it doesn’t change the strategy: - USA must out-innovate &race faster, as we have done in the entire history of AI - Tighten export controls on chips so that we can maintain future leads Every major breakthrough in AI has been American
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Introducing StarCoder ⭐️ a 15B open-source Code-LLM created by @huggingface and @ServiceNow through @BigCodeProject 🔡 8192 token context window 📊 trained on 1 trillion token 💭 80+ Programming languages 🔐 only permissive licensed data ✅ commercial use huggingface.co/bigcode/starc…
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Yi Z. retweeted
.@f2pool and @stakefish team heading to #Consensus2022. – at Austin, TX
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We’re changing our company name so we can give the full @Square brand to our Seller business. So now we need a name to tie @Square, @CashApp, @TIDAL, and @TBD54566975 together into one. That name is “Block.” Why?
Block is @Square, @CashApp, @spiralbtc, @TIDAL, @TBD54566975, and our foundational teams who support them. We’re here to build simple tools to increase access to the economy. block.xyz/
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If you can buy happiness, buy it.
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After a long wait, we are finally announcing the #Recsys2021 challenge! This year we are releasing around 1 billion samples, largest social network dataset by far, with an added focus on fair recommendations. Please checkout our website recsys-twitter.com/ for more details.
We are proud to announce the #RecSys2021 challenge! The data for this year's challenge is provided by @TwitterResearch. This year's goal is two-fold: predict different engagement types, while providing fair recommendations. More details on our website: recsys.acm.org/recsys21/chal…
Here is a free course on Machine Learning thought by Tom Mitchel and Miria-Florina Balcan. ▫️You get all the notes. ▫️You get all the videos. ▫️Tom and Maria are amazing. Course: cs.cmu.edu/~ninamf/courses/6… Just like attending Carnegie Mellon University. For free! From home!
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Recsys 2020 challenge is now concluded! We had 1000+ teams registered and top 3 teams are Nvidia, learner and Wantedly. This is probably first time in a few years that the top performance teams are not even close! Check out the results under recsys-twitter.com/final_lea….
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I’m moving $1B of my Square equity (~28% of my wealth) to #startsmall LLC to fund global COVID-19 relief. After we disarm this pandemic, the focus will shift to girl’s health and education, and UBI. It will operate transparently, all flows tracked here: docs.google.com/spreadsheets…
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We’ve made the decision to stop all political advertising on Twitter globally. We believe political message reach should be earned, not bought. Why? A few reasons…🧵
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This is why you should come work @Twitter
We’ve made the decision to stop all political advertising on Twitter globally. We believe political message reach should be earned, not bought. Why? A few reasons…🧵
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Twitter’s ad server operates at immense scale to match, rank, and deliver high quality ads in real time. Learn about recent upgrades in our latest blog post, written by the Revenue Science team! blog.twitter.com/engineering…
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📣 We're excited to announce @Fabula_AI is joining Twitter! They'll bring their machine learning talent to help us grow our research team and graph deep learning technology to improve the health of the conversation on Twitter. Welcome to the flock! blog.twitter.com/en_us/topic…
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A Twitter old-timer visiting!
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Tech Lead for ML Core Environment at Twitter Cortex, Yi Zhuang (@yz) joins us to discuss the ML landscape at Twitter, the history of the Cortex team, their recent Deepbird v2 framework, & more. Hear his interview at buff.ly/2Z6ZzqM #machinelearning #platforms
First escape room with the team. It was chaotic but we finished 11 minutes ahead of time! @timssweeney @realpriyankj @mahakp @ricardoc @yz @negroprogrammer @sdasan @cibelemh @RuhuaJiang @briacm @andrewbean
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Learn more about the #PoweredByTF ML model that ranks the tweets in your timeline! The Cortex and Home Timeline teams at @Twitter describe re-tooling their platform for TensorFlow in this guest post. #TFDevSummit Read it here ↓ bit.ly/2IY9jQS
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tiny brain: the road to hell is paved with good intentions regular brain: the road to heaven is also paved with good intentions expanding brain: the vast majority of roads are paved with good intentions galaxy brain: intentions are low caliber roadbuilding materials
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