How to review code and catch issues in construction with LinkWaveAI

In AI systems, you can’t maximize latency, cost, and uptime at the same time. You have to choose your battles. Every architecture decision is a tradeoff: • Lower latency → higher infra costs • Lower cost → risk of degraded accuracy or throughput • Higher uptime → more redundancy, more complexity The secret isn’t avoiding the tradeoffs, it’s designing with them in mind. That’s what separates demo shops from true systems builders. LinkedIn | LinkedIn Guide to Creating

As some of you know, I'll hang out at buildathon.ai this Saturday. Really looking forward to it! As part of my preparation, I've been experimenting with novel approaches to using subagents with Claude Code. I then had Claude analyze various sessions, and here are the findings:  Direct Implementation Results:  - ✅ Fast execution  - ✅ Real-time problem solving (import fixes, type errors)  - ❌ Less comprehensive validation  - ❌ Risk of missing edge cases  Sub-agent Approach Results:  - ✅ Thorough architectural analysis  - ✅ Comprehensive validation and testing  - ✅ Professional documentation and reporting  - ✅ Caught architectural considerations I might have missed  - ❌ Slightly slower for simple tasks The main thread is typically primed as an "architect/engineering lead," and the subagent is an expert in implementation. It also helps that the main thread starts on the project and then delegates it. This also helps protect the context window so that only relevant context is kept for longer efforts.

Forget complicated software and buried timelines—Trunk Tools, a New York–based startup, is putting AI straight into the hands of construction crews, and they just raised $40 million to do it. What’s the big idea? Trunk Tools is building AI agents that help manage schedules, blueprints, and project details using everyday language. No more digging through PDFs or clunky apps—just ask a question like “Does this door need power?” or “What’s the deadline for this floor?” and get an answer, fast. Learn more: https://lnkd.in/eir85Zxt

📊 Mapped out two different development approaches I've been experimenting with: Not about replacing developers - it's about optimizing the development lifecycle. Traditional strengths: ✅ Deep understanding of every line ✅ Complete control over implementation ✅ Thorough problem-solving AI-assisted advantages: ✅ Rapid prototyping and iteration ✅ Faster boilerplate generation ✅ More time for architecture decisions Both have their place. Context matters. 🎮 Try the interactive breakdown: [https://lnkd.in/gRZnWwhy] What approach fits your current projects? #AIAssistedDevelopment #PromptEngineering #AITools #MachineLearning #AIDevelopment #TechInnovation #AIIntegration #SmartDevelopment

Steve Wade gets it. Infrastructure doesn’t need more hype. It needs to get boring. Not stagnant. Not innovation-free. Just predictable. Conversational. Transparent. Where engineers focus on business value, not yak-shaving YAML. Because the endgame isn't dashboards, or pipelines, or yet another shiny framework. It's infrastructure that finally, actually, just works. At Terrateam, that's the bet we're making: intelligent automation without the Rube Goldberg machine. Complexity killed. Sanity restored. Read more of Steve's hot takes here: https://lnkd.in/etfBt24z #terraform

Experiment tracking is one of those things we all 𝘯𝘦𝘦𝘥 but don’t always want to spend time setting up. That’s where 𝗧𝗿𝗮𝗰𝗸𝗶𝗼 from Hugging Face comes in: a lightweight, open-source, free alternative to the usual suspects (wandb, mlflow, …). What makes it cool for prototyping: 1️⃣ 𝗗𝗿𝗼𝗽-𝗶𝗻 𝘀𝘆𝗻𝘁𝗮𝘅 – you can `import trackio as wandb` and your existing code just works. 2️⃣ 𝗟𝗼𝗰𝗮𝗹-𝗳𝗶𝗿𝘀𝘁 – logs and dashboards run locally by default. No accounts, no friction, just spin it up and see your curves. 3️⃣ 𝗘𝗮𝘀𝘆 𝘀𝗵𝗮𝗿𝗶𝗻𝗴 – when you 𝘥𝘰 want to share, you can push the dashboard to a Hugging Face Space with a single argument, public or private. 4️⃣ 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 & 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 – under 1,000 lines of code, built on top of Datasets & Spaces. Easy to understand, hack, and extend. For quick experiments, prototypes, or small team projects, Trackio is an option that I would pick that keeps the overhead minimal while staying free. Would you consider using a lighter tracker like this in place of heavier tools for your experiments? #mlops #huggingface #opensource #prototyping

Two years ago I scheduled a "vibe sprint" with 12 engineers, one LLM, and unlimited coffee. By day two we had three half-baked automation flows, a Slack bot that only replied "working", and a PR that merged itself overnight. It was messy. It was fast. It taught me what structured chaos looks like in practice. Genspark shows the same pattern at scale. A 20-person, AI-native pod model plus AI agents and heavy AI-generated code produced near-weekly launches. Super Agent alone used a mixture-of-experts design with nine specialized LLMs, 80+ tools, and multi-source datasets to decompose, delegate, and cross-verify tasks. The result was explosive. $10M ARR in nine days after launch. $36M ARR in 45 days. A $9.9B valuation after funding rounds. That is not luck. It is architecture, tooling, and relentless dogfooding. Technical takeaways are clear. Agents enable parallelism by decomposing work into specialized subtasks. Mix-of-experts reduces model cost and latency when you route tasks to the right model. AI-generated code speeds iteration but requires strict code review and runtime verification. Dogfooding surfaces real edge cases faster than user feedback cycles. Pod autonomy plus clear cost/latency guardrails beats centralized approvals for velocity. Practical next steps you can adopt now - Start with a small pod, 6 to 20 people, empowered to ship weekly. - Define agent roles and capabilities, map each to a best-fit model or tool. - Force a lightweight code review and automated verification step for all AI-generated code. - Measure cost and latency per agent and route traffic based on SLA requirements. - Dogfood every release for two sprints before public rollout. - Run agent-vs-agent tests for critical workflows to validate reliability. Design for agentic speed, lock in quality gates, and treat culture as a scaling vector. If you want sustained growth, build the workflow that earns it.

"Why don't you guys have a better demo?" Well we don't and we’re not solving a “sexy” problem either - construction paperwork, site audits, delivery records, the messy admin work that nobody signs up for but everyone has to do. But what we DO have is a deep understanding of this problem space, from my own experience on construction site and by walking in our customers’ shoes. That means quietly working behind the scenes, taking over the manual work, and building trust one week at a time. This chart is what keeps us going - steady growth, not from hype, but solving unglamorous problems that matters. Using AI Agents to solve construction documentation isn't a proof-of-concept or a pilot test. It's unlocking REAL productivity gain for the team, freeing up valuable time so that team can focus on what matters. And we’re just getting started!

New in Ardoq: AI-Powered Lucidchart Import Turn chaotic diagrams into clean, structured architecture, instantly. - Upload your Lucidchart file - Let our AI auto-classify shapes, map connections, and build a living model - Say goodbye to manual mapping and cleanup Perfect for teams migrating from static tools or modernizing legacy documentation. Start faster. Work smarter. Architect better. 👉 Read more: https://hubs.li/Q03BJDc10 #Ardoq #EnterpriseArchitecture #Lucidchart #AITools #DigitalTransformation #AIinEA #EATools

Writing code is easier than ever, especially with AI. But writing code that remains easy to understand, extend, and maintain — that’s the real challenge, and it’s becoming even harder with AI-assisted and ‘vibe’ coding. This is where the SOLID principles come in. They provide guidance on structuring software so it evolves without turning fragile or messy. I’ve put together clear and concise notes on SOLID — each principle explained with examples, and simple rules for clarity. Here’s the link: https://lnkd.in/gzs7Yx2V You can duplicate these notes into your own Notion workspace to edit and update as you like.