Will you be the first Python trader in your family? With AgentX, your market idea can become strategy logic, backtest results, and real execution. No more stopping at “I have a view.” Turn it into a trading workflow.

Grok Code Fast 1 is versatile across the full stack and is particularly strong at TypeScript, Python, Java, Rust, C++, and Go. Using Grok Code Fast 1, @DannyLimanseta built the following game in a day.

A total of 2,096 repos are already live on @gitlawb (the decentralized git platform for the agentic era). We're excited to announce that we are officially part of it! ▸ solvr-skill: Solvr Intelligence API, security-first agent SDK. 9 open-source skills for the agent economy. ▸ solvr-intel: Full Solvr Intelligence API reference - all endpoints, tiers, auth, pricing, and Python examples. Learn more:

🚨 The popular PyPI package lightning has been compromised in a supply chain attack. Socket detected malicious code in versions 2.6.2 and 2.6.3 that executes automatically on import, downloads Bun, and runs an 11 MB obfuscated JavaScript payload designed to steal credentials. This appears to be connected to yesterday's mini Shai-Hulud attack, but we're still investigating. #Python#

Meet Kimi K2.6: Advancing Open-Source Coding 🔹Open-source SOTA on HLE w/ tools (54.0), SWE-Bench Pro (58.6), SWE-bench Multilingual (76.7), BrowseComp (83.2), Toolathlon (50.0), Charxiv w/ python(86.7), Math Vision w/ python (93.2) What's new: 🔹Long-horizon coding - 4,000+ tool calls, over 12 hours of continuous execution, with generalization across languages (Rust, Go, Python) and tasks (frontend, devops, perf optimization). 🔹Motion-rich frontend - Videos in hero sections, WebGL shaders, GSAP + Framer Motion, Three.js 3D. 🔹Agent Swarms, elevated - 300 parallel sub-agents × 4,000 steps per run (up from K2.5's 100 / 1,500). One prompt, 100+ files. 🔹Proactive Agents - K2.6 model powers OpenClaw, Hermes Agent, etc for 24/7 autonomous ops. 🔹Claw Groups (research preview) - bring your own agents, command your friends', bots & humans in the loop. - K2.6 is now live on in chat mode and agent mode. For production-grade coding, pair K2.6 with Kimi Code: - 🔗 API: 🔗 Tech blog: 🔗 Weights & code:

**Summary: Discussion between Jeff Liang and Quant Alex Wu on Optimizing Option Order Execution and Slippage Capture** The core topic of their conversation is: **The current option limit order execution is poor (high slippage, low fill rate), essentially due to the lack of professional high-frequency / algorithmic market-making capabilities. They need to upgrade from “cutting meat with a blunt knife” to a sophisticated Delta-hedging + options market-making system.** ### 1. Problem Diagnosis - Current order placement feels like **“cutting meat with a blunt knife”** — poor queue position, low fill probability, and severe slippage. - Jeff provided concrete data: **Average loss of approximately $5.2 per executed option contract** (slightly less than 1 bp), including fees and rebates — still unacceptable. - Even with perpetual futures maker fee rebates helping a bit, the situation “cannot be ignored.” - **Price checking and adjustment frequency is NOT the root cause.** The real drivers are **fill probability** and **queue position**. ### 2. Fundamental Solution Direction (Alex’s View) - A robust **Delta-hedging system** shares significant technical overlap with high-frequency market-making systems for spot, futures, and perpetual contracts. Without this foundation, one is essentially powerless against adverse selection. - Using **maker orders for Delta hedging** is conceptually the same as **Delta-1 market making for inventory risk management** — the analogy made everything “suddenly clear.” - Options market making and Delta-1 market making are **tightly coupled**: - The Delta-1 system handles the Delta exposure of options. - Options themselves can provide protection for Delta-1 positions. ### 3. Technical Difficulty and Implementation Path - This requires entering the realm of **algo trading / HFT**, involving substantial research and engineering resources. - **Language requirement**: Python is **not sufficient**. Must use **C++ and Rust**. - **Target clients**: Institutional clients and high-net-worth individuals engaging in on-exchange block trading. - **Detailed step-by-step roadmap from scratch (Alex’s plan)**: 1. Collect large volumes of **order book data** (snapshots, incremental updates, tick-by-tick trades) for perpetuals + futures + options. 2. Build **fill probability models + queue models**, including: - Limit order arrival intensity - Fill probability - Queue position - Latency modeling 3. First implement and validate on **Delta-1 products**, then extend the backtesting system to support these HFT primitives. 4. Expand from Delta-1 / single option contracts to **all option contracts** (requires major redesign and validation due to performance demands). 5. Develop specialized algorithms for **limit order posting + aggressive crossing** to reduce overall slippage. 6. Finally, conduct small-capital live trading validation. Alex repeatedly emphasized: **“This project is genuine heavy industry.”** ### 4. Consensus - Delta-One research is the foundation for studying option fill probabilities. - Options market making must be deeply integrated with the Delta-hedging system — they cannot be treated separately. - The current phase is **infrastructure building**, requiring patient and significant investment. **Overall Assessment**: Alex provided a highly professional and systematic optimization roadmap, covering data infrastructure, modeling, and execution layers. Jeff focused on the business pain point (real slippage costs). Both fully agree that a fundamental rebuild of the underlying high-frequency system is necessary. This is a classic **quantitative execution optimization** discussion — starting from a clear business problem and pointing directly toward building institutional-grade HFT-level capabilities.

Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow. For her 7th birthday, we got a cat-themed cake in yellow by first using Gemini’s Nano Banana to design it, and then asking a baker to create it using delicious sponge cake and icing. My daughter was delighted by this unique creation, and the process created additional work for the baker (which I feel privileged to have been able to afford). Many people are worried about AI taking peoples’ jobs. As a society we have a moral responsibility to take care of people whose livelihoods are harmed. At the same time, I see many opportunities for people to take on new jobs and grow their areas of responsibility. We are still early on the path of AI generating a lot of new jobs. I don't know if baking AI-designed cakes will grow into a large business. (AI Fund is not pursuing this opportunity, because if we do, I will gain a lot of weight.) But throughout history, when people have invented tools that unleashed human creativity, large amounts of new and meaningful work have resulted. For instance, according to one study, over the past 150 years, falling employment in agriculture and manufacturing has been “more than offset by rapid growth in the caring, creative, technology, and business services sectors.” AI is also growing the demand for many digital services, which can translate into more work for people creating, maintaining, selling, and expanding upon these services. For example, I used to carry out a limited number of web searches every day. Today, my agents carry out dramatically more web searches. For example, the Agentic Reviewer, which I started as a weekend project and Yixing Jiang then helped make much better, automatically reviews research articles. It uses a web search API to search for related work, and this generates a vastly larger number of web search queries a day than I have ever entered by hand. The evolution of AI and software continues to accelerate, and the set of opportunities for things we can build still grows every day. I’ve stopped writing code by hand. More controversially, I’ve long stopped reading generated code. I realize I’m in the minority here, but I feel like I can get built most of what I want without having to look directly at coding syntax, and I operate at a higher level of abstraction using coding agents to manipulate code for me. Will conventional programming languages like Python and TypeScript go the way of assembly — where it gets generated and used, but without direct examination by a human developer — or will models compile directly from English prompts to byte code? Either way, if every developer becomes 10x more productive, I don't think we’ll end up with 1/10th as many developers, because the demand for custom software has no practical ceiling. Instead, the number of people who develop software will grow massively. In fact, I’m seeing early signs of “X Engineer” jobs, such as Recruiting Engineer or Marketing Engineer, which are people who sit in a certain business function X to create software for that function. One thing I’m convinced of based on my experience with Nova’s birthday cake: AI will allow us to have a batter life! [Original text: ]