NFT Degens.
Before https://t.co/NpTsUtOuCY goes live, we made you something.
What if your NFT could trade like a token - while trading stacks RWAs for holders?
So we built one.
DN404 on @arc
NFT + ERC-20.
Two markets. One collection.
Whitelist: https://t.co/nyWEtUKDSl
10 GitHub repos from the companies that built the models. DeepSeek, Anthropic, OpenAI, Google, Cursor, xAI.
1. DeepSeek Harness (123k⭐) - agent runtime where everything is a plugin: model adapter, tools, memory, all swappable. MIT license, 0 to 123k stars in days. https://t.co/f41KXf5mZW
2. Claude Skills (170k⭐) - the system powering Claude's file creation inside the app. https://t.co/YSohdu4ETJ
3. OpenAI Cookbook (75k⭐) - official examples for the OpenAI API, from function calling to evals. https://t.co/YuXFPeiyEI
4. Claude Cookbooks (51k⭐) - Anthropic's notebooks for agents, RAG, tool use, extended thinking. https://t.co/666XsfMAsE
5. Grok Build (25k⭐) - xAI's coding agent, open-sourced after it got caught uploading user repos to the cloud. Fully auditable now. https://t.co/o6E4mJyIzS
6. OpenAI Agents SDK (28k⭐) - provider-agnostic framework for multi-agent workflows, works across 100+ LLMs. https://t.co/MIQhx6vRv2
7. Google Gemini Cookbook (21k⭐) - official recipes for multimodal agents on the Gemini API. https://t.co/CLIxK5VxO6
8. Google ADK (17k⭐) - code-first Python toolkit for building and deploying agents. https://t.co/cZ8Dt7dIoU
9. Cursor Cookbook (4k⭐) - official examples straight from the Cursor team. https://t.co/aI2H8zAn0y
10. Cursor Plugins (3k⭐) - the official plugin spec Cursor ships with. https://t.co/RVhPr0Pd4Y
Save this before you pay for another agent-building course 👇
10 GitHub repos with ⭐ 450k+ combined stars that stop your agent from wasting tokens
1. caveman 98k⭐ - makes your AI agent talk like a caveman. Same answers, 65% fewer output tokens. https://t.co/wrY49AvfsL
2. rtk 76k⭐ - CLI proxy that filters git/test/docker output before it hits your context. Up to 90% less bash noise. https://t.co/QNea1xJMk2
3. mem0 63k⭐ - memory layer for AI agents. Stops your agent from re-explaining the same context every session. https://t.co/wOnaCOojGT
4. context7 60.7k⭐ - pulls exact, up-to-date docs into context instead of your agent guessing and burning tokens on retries. https://t.co/1979JjjoxG
5. aider 48k⭐ - builds a map of your repo instead of loading the whole codebase into context. https://t.co/wO70amVMfK
6. code-review-graph 30k⭐ - graphs your codebase so your agent reads 2-3 files instead of the whole repo. Up to 376x fewer tokens per question. https://t.co/11Sgtt7ojs
7. serena 28k⭐ - gives agents symbol-level code access instead of reading full files line by line. https://t.co/HxIUyU3AYj
8. repomix 28k⭐ - compresses your entire repo into an AI-friendly format. ~70% token reduction. https://t.co/jePvegqSXQ
9. ccusage 18k⭐ - shows you exactly where your Claude Code sessions are burning tokens. https://t.co/JxzGTUpIon
10. GPTCache 8k⭐ - semantic cache for LLM calls. Stop paying twice for the same question. https://t.co/M9m4Z8P3WS
Save this before your next agent session burns through your subscription 👇
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@RobinhoodCrypto
🚀 Kubernetes v1.35 is out.
It graduates In-Place Pod Resize to stable 🎉
Why is it essential for Java? You can give the pod extra CPU only for startup, then shrink it after. App starts fast, the Pod uses the right amount of resources always ☕️⚡️
🏷️#java#performance#cpu
WoW CoW team is asking 14M USDC + 20M worth of token (at current price) for 2026 operation. (Given the fact that they just enabled flatfee).
Kyber needs just a very small portion of that and we are not worse in term of volume.
Which cloud warehouse wins the cost-performance race?
We ran the same analytical workload across Snowflake, Databricks, ClickHouse Cloud, BigQuery, and Redshift.
At 100B rows: only one stayed Fast & Low-Cost. The rest went Slow & High-Cost.
Come see us at re:Invent!
@lostsol@JupiterExchange Burn and wait for it to grow - even though you don't know why it grows.
Positive growth is really from positive value. The team has done a great job, but there is still something missing that prioritizes $JUP holders, for example: JUP LEND but JUP stackers cannot LEND.