Jiawei Guan’s Post

AI is both spear and shield—and right now, the spear is getting sharper faster than we can forge the shield. Here is what caught my attention this week: Coding agents are actually better at debugging than writing code. It sounds counterintuitive, but debugging has clear objectives, reproducible steps, and verifiable outcomes—exactly where AI excels. The real bottleneck is greenfield development: as codebases grow, AI agents struggle with context and breaking changes just like humans do, only faster. Meanwhile, creativity-driven open source is having a moment. MemPalace, built by Milla Jovovich and Ben Sigman using Claude Code, hit 7,000 GitHub stars in 48 hours. It achieved 96.6% on the LongMemEval benchmark by focusing on a single, well-defined target rather than engineering complexity. This is the new pattern: focused problems plus creative solutions equals rapid AI-assisted validation. But the biggest wake-up call is security. Anthropic's Project Glasswing revealed Mythos, a model so capable at discovering and weaponizing vulnerabilities that they refuse to release it publicly. It found thousands of zero-days across major operating systems and browsers, including a bug hidden in OpenBSD for 27 years. OpenAI's GPT-5.4 just earned their first "High Cybersecurity Risk" rating. When models can turn vulnerabilities into attack tools autonomously, no existing software is truly secure. This changes how we should build products. Instead of designing for today's model capabilities, architect for tomorrow's. Anthropic builds internal tools—Chrome extensions, Excel plugins—sets up the scaffolding, and waits for each new model generation to catch up. If you design for current limits, your product is obsolete at launch. Build the framework first, then let the engine arrive. The game continues: Zhipu just open-sourced GLM-5.1 under MIT license, scoring higher than GPT-5.4 on SWE-Bench Pro while raising prices 10% against the industry trend. Nobody is retreating from the open-source race yet. Read the full article: https://lnkd.in/gVPRiU46 #AI #SoftwareDevelopment #Cybersecurity #ProductStrategy #OpenSource

We have spent months debating whether AI writes better code than it debugs. I think we have it backwards. Current coding agents are actually better at debugging than writing from scratch. Debugging has clear objectives and reproducible steps. Building a complete product from zero? That is where things fall apart. Prototypes feel fast and satisfying, but maturity exposes edge cases, context limits, and compounding complexity. AI does not escape the laws of large-project maintenance. But there is a sweet spot: creativity-driven, narrowly scoped open source projects. MemPalace, built by Milla Jovovich and engineer Ben Sigman using Claude Code, hit 7,000 GitHub stars in two days and scored 96.6% on LongMemEval, outperforming paid solutions. It is lightweight, local, and MIT-licensed. The lesson: the more focused the problem, the faster AI helps you validate an idea. Then there is the security reality check. Anthropic's leaked model Mythos discovered thousands of zero-days across major operating systems, including a 27-year-old bug in OpenBSD, and can weaponize them. Anthropic is withholding it and sharing access with 45 tech giants to harden defenses first. Before it becomes a spear, let it serve as a shield. Meanwhile, OpenAI's own GPT-5.4 scored 76% on CTF competitions and earned a High Cybersecurity Risk rating. No existing software is safe in front of these capabilities. So how should we build? Slightly ahead of the models. If you design only for today's capabilities, your product will be obsolete at launch. Build the architecture and framework now, test each new model generation against it, and invest heavily when the engine finally arrives. And in case you thought the open-source race was cooling down: Zhipu's GLM-5.1 just went fully open source under MIT, topped GPT-5.4 and Claude Opus 4.6 on SWE-Bench Pro, and raised prices by 10% while everyone else is cutting them. The game is still on. Read the full article: https://lnkd.in/gVPRiU46 #ArtificialIntelligence #CyberSecurity #SoftwareDevelopment #ProductStrategy #OpenSource

https://lnkd.in/dCitVqcD Headline: 4 Generations in 3 Years: Is the "AI Treadmill" finally moving too fast? 🏃♂️💨 NetworkChuck recently released a video titled "I kind of hate AI," and the sentiment is echoing across my entire feed. It’s called "AI Burnout", the anxiety of constant news, the fear of displacement, and the decision paralysis that comes with a landscape changing every 48 hours. But history has a way of calming the nerves. Just like the printing press didn’t end books and the car didn't end transportation, AI isn't deleting jobs. We aren't seeing less work; we're seeing a shift from being "The Creator" to being "The Orchestrator." In my latest blog post, I dive into: The Software Pivot: Why I’ve officially moved away from OpenClaw in favor of Hermes. When things break, you need maintainable Python architecture, not a sea of GitHub issue duplicates. The Generational Leap: We’ve moved past simple chat windows and IDE plugins. We are now entering the 4th generation: The Orchestrator era, where you manage the agentic loops. The Local Revolution: Why the Qwen3.5 27b or Gemma 4 31B model is the current "Goldilocks" model for those of us running local hardware. They outperform old giants like Deepseek R1 while running on consumer-grade GPUs. The pace is exhausting, but if you stop trying to master every tool and start mastering the architecture, the treadmill stops being a threat and starts being a playground. #AI #TechTrends #OpenSource #LLM #Linux #CyberSecurity #Gemma4 #NetworkEngineering

🤖 Claude Code Leak: A Masterclass in AI Engineering The entire source code for Claude Code was recently leaked via an npm sourcemap. While the security slip is a reminder to double-check your .npmignore, the code itself is a fascinating look at the future of AI agents. Behind the scenes, the engineering is brilliant: ULTRAPLAN: When a task is too complex, the CLI offloads it to a remote Cloud Container. It can "think" for 30 minutes before "teleporting" the results back to your local terminal using the __ULTRAPLAN_TELEPORT_LOCAL__ sentinel. The "Dream" System: A background subagent that consolidates memories while you're away, literally "dreaming" to turn messy logs into long-term knowledge. Multi-Agent Orchestration: A sophisticated "Coordinator" mode that spawns parallel workers to research, write, and verify code simultaneously. It’s rare to get a look at how the best in the industry build "alive" tools. Security fail? Yes. But the architecture is a work of art. Read the full breakdown: https://lnkd.in/d85m8Eth #AI #SoftwareEngineering #ClaudeCode #Anthropic #Coding #TechNews #Programming #hacked #sourcecode #claudeleak #leakedcode

The AI landscape just shifted let’s move beyond the “chatbot” mindset 🚀 Anthropic’s Project Glasswing, powered by the Mythos-class model, isn’t just predicting the next token. It's introducing a new breed of problem-solving AI. As a Product Engineer, I am witnessing the shift from basic "Chain of Thought" processes to Recursive Self-Correction Loops. This technology helped uncover a 27-year-old vulnerability in OpenBSD and it's precisely why Anthropic is cautious about releasing the keys. In my latest article, I dive into: ✅ Why the "Capybara" scaling tier revolutionizes long-horizon reasoning ✅ The shift from "Line-by-Line" coding to System Orchestration ✅ Why "Bulletproof" code is quickly becoming an automated reality If you’re building in the mobile or product space, understanding how Search Based Inference will change your workflow is critical. Read the full engineering breakdown here: 🔗 https://lnkd.in/dwRttQBB #Anthropic #ProjectGlasswing #ProductEngineering #CyberSecurity #GenerativeAI #AI #Innovation #MachineLearning #TechTrends

🧠 Master AI Agents – The Only Roadmap You'll Actually Need Most people jump straight into building AI agents without understanding the full stack. That's like building a house without a foundation. This visual roadmap saved me months of scattered learning. Here's what actually matters, layer by layer 👇 1️⃣ Programming Foundation – Python, async, APIs, Pydantic. Nothing works without this. 2️⃣ LLM Fundamentals – Tokens, context windows, function calling. Know your engine. 3️⃣ Prompt Engineering – Chain-of-Thought, ReAct, role prompting. Garbage in, garbage out. 4️⃣ Agent Core Architecture – The agent loop, memory, planning vs reacting. This is where agents come alive. 5️⃣ Tool & Function Calling – JSON schemas, API orchestration, error retries. Agents need hands. 6️⃣ Memory & RAG – Embeddings, vector databases (Pinecone, Chroma, Qdrant), long-term vs short-term memory. Without memory, every conversation starts from zero. 7️⃣ Multi-Agent Systems – Planner-executor, supervisor agents, swarms. One agent is smart. Multiple agents are powerful. 8️⃣ Evaluation & Guardrails – Hallucination detection, red teaming, output validation. Trust is not optional. 9️⃣ Observability – Token usage, latency, cost per request. You can't improve what you don't measure. 🔟 Deployment & Scaling – Async workers, queues, Docker, Kubernetes. Moving from notebook to production. 1️⃣1️⃣ AI Gateway & Security – Rate control, prompt filtering, content safety. Build agents that are safe, not just smart. What surprises most people? The hardest part isn't the LLM. It's memory, tool calling, guardrails, and observability. Two questions for you: Which layer are you currently stuck on? And what would you add to this roadmap? Let's learn in public. Drop your thoughts below 👇 #AgenticAI #AIAgents #MachineLearning #LLM #RAG #LangChain #AutoGen #GenerativeAI #AIEngineering #LearningInPublic

🧠 Master AI Agents – The Only Roadmap You'll Actually Need Most people jump straight into building AI agents without understanding the full stack. That's like building a house without a foundation. This visual roadmap saved me months of scattered learning. Here's what actually matters, layer by layer 👇 1️⃣ Programming Foundation – Python, async, APIs, Pydantic. Nothing works without this. 2️⃣ LLM Fundamentals – Tokens, context windows, function calling. Know your engine. 3️⃣ Prompt Engineering – Chain-of-Thought, ReAct, role prompting. Garbage in, garbage out. 4️⃣ Agent Core Architecture – The agent loop, memory, planning vs reacting. This is where agents come alive. 5️⃣ Tool & Function Calling – JSON schemas, API orchestration, error retries. Agents need hands. 6️⃣ Memory & RAG – Embeddings, vector databases (Pinecone, Chroma, Qdrant), long-term vs short-term memory. Without memory, every conversation starts from zero. 7️⃣ Multi-Agent Systems – Planner-executor, supervisor agents, swarms. One agent is smart. Multiple agents are powerful. 8️⃣ Evaluation & Guardrails – Hallucination detection, red teaming, output validation. Trust is not optional. 9️⃣ Observability – Token usage, latency, cost per request. You can't improve what you don't measure. 🔟 Deployment & Scaling – Async workers, queues, Docker, Kubernetes. Moving from notebook to production. 1️⃣1️⃣ AI Gateway & Security – Rate control, prompt filtering, content safety. Build agents that are safe, not just smart. What surprises most people? The hardest part isn't the LLM. It's memory, tool calling, guardrails, and observability. Two questions for you: Which layer are you currently stuck on? And what would you add to this roadmap? Let's learn in public. Drop your thoughts below 👇 #AgenticAI #AIAgents #MachineLearning #LLM #RAG #LangChain #AutoGen #GenerativeAI #AIEngineering #LearningInPublic

🧠 Master AI Agents – The Only Roadmap You'll Actually Need Most people jump straight into building AI agents without understanding the full stack. That's like building a house without a foundation. This visual roadmap saved me months of scattered learning. Here's what actually matters, layer by layer 👇 1️⃣ Programming Foundation – Python, async, APIs, Pydantic. Nothing works without this. 2️⃣ LLM Fundamentals – Tokens, context windows, function calling. Know your engine. 3️⃣ Prompt Engineering – Chain-of-Thought, ReAct, role prompting. Garbage in, garbage out. 4️⃣ Agent Core Architecture – The agent loop, memory, planning vs reacting. This is where agents come alive. 5️⃣ Tool & Function Calling – JSON schemas, API orchestration, error retries. Agents need hands. 6️⃣ Memory & RAG – Embeddings, vector databases (Pinecone, Chroma, Qdrant), long-term vs short-term memory. Without memory, every conversation starts from zero. 7️⃣ Multi-Agent Systems – Planner-executor, supervisor agents, swarms. One agent is smart. Multiple agents are powerful. 8️⃣ Evaluation & Guardrails – Hallucination detection, red teaming, output validation. Trust is not optional. 9️⃣ Observability – Token usage, latency, cost per request. You can't improve what you don't measure. 🔟 Deployment & Scaling – Async workers, queues, Docker, Kubernetes. Moving from notebook to production. 1️⃣1️⃣ AI Gateway & Security – Rate control, prompt filtering, content safety. Build agents that are safe, not just smart. What surprises most people? The hardest part isn't the LLM. It's memory, tool calling, guardrails, and observability. Two questions for you: Which layer are you currently stuck on? And what would you add to this roadmap? Let's learn in public. Drop your thoughts below 👇 #AgenticAI #AIAgents #MachineLearning #LLM #RAG #LangChain #AutoGen #GenerativeAI #AIEngineering #LearningInPublic

AI is better at debugging your code than writing it from scratch. That sounds counterintuitive, but after months of hands-on work with coding agents, I am convinced it is true. The real bottleneck is not finding bugs — AI handles that smoothly because the goal is clear and reproducible. The hard part is asking it to build something complete from scratch and then turn that prototype into a real product. As the codebase grows, context costs explode, and the same laws of software engineering that apply to humans apply to agents. Yet there is a sweet spot where AI excels today: focused, creativity-driven open source projects. Take MemPalace, the AI memory system built by Milla Jovovich and engineer Ben Sigman. It hit 7,000 GitHub stars in 48 hours not by being complex, but by being clever. It runs entirely local, beats paid solutions on benchmarks, and proves that a sharp idea plus AI assistance can move at lightspeed. Then there is the other side of the coin: security. Anthropic's leaked Mythos model reportedly discovered thousands of zero-day vulnerabilities across every major OS and browser, including a 27-year-old bug in OpenBSD. It does not just find flaws — it weaponizes them. Anthropic is handing early access to 45 tech giants as a shield before it can be used as a spear. Meanwhile OpenAI's GPT-5.4 just became its first model rated "High Cybersecurity Risk." These converging forces are reshaping product strategy. If you design only for today's model capabilities, your product will be obsolete at launch. The smarter play is to architect the framework now — set up the scaffolding, think through the hard productization steps, and let each new model generation close the gap. Build the structure first, then wait for the engine to arrive. Read the full article: https://lnkd.in/gVPRiU46 #AI #SoftwareDevelopment #Cybersecurity #ProductStrategy #OpenSource

Most people think AI source code stays locked down. Last week proved otherwise. Anthropic accidentally shipped a debugging file inside a routine Claude Code update on npm. Within hours, a security researcher spotted it. The full source code was out. 512,000 lines of TypeScript. Nearly 2,000 files. Developers mirrored it on GitHub. Studied the architecture. Then one developer rewrote the core features in Python from scratch overnight and published it. Not a copy. A clean-room rebuild. And it became one of the fastest growing repositories in GitHub history. I build with Claude Code as a non-technical user. So I paid close attention. Because here is what stood out to me: The code behind this tool looked messy. Multiple languages. Inconsistent patterns. Thin error handling. Not what people expected from a product generating billions in revenue. But that is how real products get built. Quick fixes. Iteration. Constant improvement. Not polished systems. Not perfect guardrails. What happened next is the part worth watching: Weak points got exposed. Anthropic responded within hours. DMCA takedowns went out. New packaging safeguards were announced. The system improved because of the mistake. What this means if you are building in health tech or any space touching AI: The tools you rely on are still being shaped in real time. Even the ones that feel advanced on the surface. That is not a reason to stop building. It is a reason to stay informed about what is under the hood. Does knowing this make you trust these tools more or less? ___ 👋🏾 Hi, I'm Joyce! I'm building in FemTech and health tech while learning AI and product management and sharing practical insights along the way. ✨ Follow for practical insights and resources each week.