vignesh pandi
1.2K posts
@vigneshpandi01
@Flexport, ex-Twitter, ex-Google
Google's CEO "any solo developer with Claude can now outcompete a 10-person Google team" he's right but 90% of developers using Claude Code daily are starting from zero every session no stack context. no memory. no behavior rules. $975 wasted per developer every single week Sundar is talking about the future. here's the setup that makes the present actually work:
ANTHROPIC ENGINEER DROPPED A 14-MINUTE GUIDE. This is the fastest way to understand how real agents are built. Bookmark this for the weekend. 14 minutes. Real architecture. No fluff. What actually works. Agents → Structure → Tools → Execution → Systems → Money
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
Anybody using Claude Code needs to read this. 'Skills' are the single biggest hack to get more out of Claude. Anthropic just cataloged every skill they use internally and found they all fit into 9 categories. After reviewing hundreds of skills, here's their top 9: 1. Library & API reference (edge cases Claude gets wrong) 2. Product verification (automated testing with video recording) 3. Data fetching & analysis (connecting to your monitoring stack) 4. Business process automation (standup posts, ticket creation) 5. Code scaffolding & templates 6. Code quality & review (spawns a "fresh eyes" subagent to critique code) 7. CI/CD & deployment (babysits PRs, retries flaky CI, auto-rollback) 8. Runbooks (takes a symptom, walks through investigation, writes the report) 9. Infrastructure ops (finds orphaned resources, gets confirmation, cleans up) The insight: most individuals or teams have skills in maybe 2-3 of these categories If your org is only using skills for code scaffolding and style enforcement, you're leaving 80% of the value on the table
I recorded a 50-min tutorial on prompting top-tier landing pages with Gemini 3
Introducing Veo 2, our new, state-of-the-art video model (with better understanding of real-world physics & movement, up to 4K resolution). You can join the waitlist on VideoFX. Our new and improved Imagen 3 model also achieves SOTA results, and is coming today to 100+ countries in ImageFX.
India will never forget your contribution , Will miss you #RatanTata sir 🙏
