How I Supervise Three Engineering Workflows at Once Three Workflows, One Operator Right now, I have three engineering workflows open. One is under council review. Four AI roles are challenging an architectural proposal, and I will need to decide which objections actually change the plan. The second is already in implementation. That one does not need me at the moment. The specification is approved, the boundaries are clear, and the executor can keep moving. The third has come back from audit. The findings are valid, but corrective work is paused. A remediation plan exists, and someone other than the executor needs to review it before any more code changes. This is the part that still feels new: I can move between all three without reopening old chats and rebuilding the story in my head. A few months ago, even one workflow could take most of my attention. I carried context between every stage: rewriting role prompts, moving decisions between conversations, tracking the current document, and turning audit findings into the next round of work. The AI council itself was already useful. It produced strong reasoning and exposed assumptions I would probably have missed. But I was still the glue around it. The council improved the decisions. The system around it made those decisions easier to carry into implementation, audit, and correction without losing control. Conversations Were No Longer the Workflow The main change was simple to describe: I stopped treating the workflow as a series of conversations. Chats are good for thinking. They are not a good place to keep authority. Before this change, a decision might exist somewhere in a long discussion. The next agent had to interpret it, and I had to remember whether it was final, provisional, or already replaced. Now the state of the work lives in a small set of artifacts. Evidence becomes a source-grounded brief. Decisions become an approved specification. The specification becomes bounded implementation. The implementatio

I have a confession to make. When I first saw that Tether—the behemoth behind the $110 billion USDT stablecoin—was the primary financial backer of Holepunch, Keet, and the Bare JavaScript runtime, my brain short-circuited. I struggled with the cognitive dissonance. Why? Because if you have ever written a smart contract that interacts with USDT on Ethereum Mainnet, you know it is an absolute nightmare. Before we can talk about Tether’s brilliant vision for a decentralized, serverless future, we have to talk about the trauma they inflicted on a generation of Solidity developers. 1. The Original Sin: USDT is not actually an ERC-20 When you are deep in protocol-level engineering, you rely on standards. EIP-20 explicitly states that a token's transfer and transferFrom functions must return a boolean value to indicate success or failure. // The standard EIP-20 Interface interface IERC20 { function transfer(address to, uint256 amount) external returns (bool); } Tether completely ignored this. When they deployed the USDT contract, they omitted the return value entirely. Their functions return void. // The actual USDT Mainnet Implementation (simplified) function transfer ( address to , uint value ) public { // ... logic ... // Notice: No return statement. } If you blindly write a vault or a swap contract using the standard IERC20 interface to move USDT, your transaction will seamlessly execute the logic, move the funds, and then violently revert at the very last microsecond. Why? Because modern Solidity uses a strict ABI decoder. When your contract calls USDT.transfer(), the EVM executes a low-level CALL. When the call finishes, Solidity checks the RETURNDATASIZE. Since it expects a bool (32 bytes), but USDT returns absolutely nothing (0 bytes), the decoder panics and reverts the entire transaction. For years, the only way to build DeFi safely with USDT has been to wrap it in OpenZeppelin's SafeERC20 library, which uses low-level assembly to explicitly check the return data

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. You've been using the terminal for months/ years. Maybe you cd into a folder, ls around, run your script, and call it a day. That's fine. That's like knowing how to boil water and calling yourself a chef. But the terminal has layers . It's basically an onion that occasionally makes you cry, usually around 12 AM when a script fails silently and you have no idea why. So grab your coffee and let's talk about the command line tricks that actually make your life better. Not the "did you know ls -la shows hidden files" tier tips. Your Terminal Has A Memory. Use It. Most devs mash the up arrow like it's 2007 and they're trying to beat a Flash game. Stop that. Press ctrl-r instead. It searches your command history live. Type a few letters, it finds the last matching command. Press ctrl-r again to cycle back further. Found it? Hit Enter to run it, or the right arrow to drop it into your prompt so you can edit it first. ctrl-r ( reverse-i-search ) ` docker run ` : docker run -it --rm -v $( pwd ) :/app node:20 bash Pair this with ctrl-w (delete last word) and ctrl-u (nuke the line back to the cursor) and you'll start editing commands like you're speedrunning a text adventure. And if you're the type who types a whole essay of a command and then realizes you forgot something at the start, ctrl-a jumps to the beginning of the line and ctrl-e jumps to the end. No more holding the left arrow key like it owes you money. Pro tip: if you're a TUI fan and ctrl-r's default search feels a bit flat, check out McFly xargs Is The Friend Who Actually Shows Up Pipes ( | ) are great. They pass output from one command into another. But sometimes you don't want to pass output as input , you want to pass it as arguments . That's where xargs comes in, and once you get it,

Two months ago I picked up a ticket that looked routine: a job that reads mailbox data from Microsoft 365 through EWS, running fine for over a year, started failing on a subset of mailboxes in one tenant. Same app registration, same code path, same service account. The error in the logs pointed at throttling, so that's where the admin had already spent three days looking. Wrong direction. The actual cause had nothing to do with throttling budgets. This mix-up happens constantly right now, and it's worth understanding why, because the fix for one problem does nothing for the other, and chasing the wrong one wastes days. TL;DR: if your EWS failures don't scale with request volume, stop tuning throttling and go check your Application Access Policy scope groups instead. What EWS throttling actually looks like Exchange Online throttles EWS the same way it always has: budget-based. Every account gets a policy (the default is EwsDefaultThrottlingPolicy , but plenty of tenants layer custom ones on top) that tracks a slowly-refilling budget rather than a simple call count. When you overspend it, you get back a 503 or 429 with an X-MS-Diagnostics header telling you which budget got exhausted, usually the connection count or the concurrent-request limit. The tell for real throttling is consistency. It scales with load, correlates with concurrency and batch size, and clears up within minutes once you back off. If you graph failure rate against request volume, you'll see a clean relationship. If you double your batch size, failures increase. If you throttle yourself proactively (respecting Retry-After , staying under EWSFindCountLimit for FindItem calls), it mostly goes away. That correlation is the whole diagnostic test. If your failures don't scale with volume, you're not looking at a throttling problem, no matter what the error message on the surface says. What actually changed Over the past year or so, Microsoft tightened enforcement in two places that both produce errors ea

Turn NotebookLM into a complete research workflow Discussion | Link

Better to sacrifice a part to preserve the whole. — The 36 Stratagems, Sacrifice the Plum Tree to...

ive been seeing a recurring claim that open (weight) models 6 months behind the frontier are good enough for the majority of ‘work’. if you've had a concrete task in the last month where GLM/DeepSeek/Kimi/Qwen failed and Opus/Fable/GPT succeeded (or vice versa!), please share Ill provide a template as ive also frequently seen posters complain about a lack of context: - task (short and specific) - cheap model tried & how it failed - frontier model & did it actually succeed - would frontier-1 have

Solid-state batteries are safer and more capable—but harder to mass-produce. They also represent an opportunity for non-Chinese companies to get back in the game.

Approve AI requests from your lock screen Discussion | Link

Fizz has expanded its lawsuit against rival Sidechat, alleging that a Maveron VC shared its confidential information obtained during a fundraising meeting with the competing startup.