Happy Labor Day. Today, we recognize the hardworking men and women whose dedication helps build stronger communities and a stronger country. From the Munro team, thank you for your hard work and commitment. #Innovation# #Manufaturing# #Enginneering# #LeanDesign# #LaborDay# #Munro# #MunroLive#

Engineering, Product, Marketing, each in its own silo, with its own context OpenClaw Multiplayer gives teams one shared agent and shared context, so they can actually work together @Pishgar on what we showed at GitHub Universe:

Engineering leaders told Grepr founder and CEO Jad Naous they spend 5 to 20% of their infrastructure budget on observability, and it's buying less and less reliability. Jad, a former a16z partner, joins a16z's Martin Casado to discuss whether AI can run production systems as well as it writes code. Grepr sits in a company's log stream, can scan a petabyte of logs a day, cuts observability costs by about 90%, and is building AI agents that use those signals to troubleshoot production issues. How AI is changing engineering at Jad's 10-person company: - A data lake feature that once took one engineer about six months took Jad 10 days - The gap between okay and great engineers on his team "just multiplied 10X" - He runs 3-4 agent sessions at once, with a "tech lead" agent reviewing every PR Why running live systems is a harder problem for AI: - Jad: AI can almost tune databases, but can't yet keep servers running on its own - Martin: every bit of code doubles the ways a program can behave, which is why humans still own systems - The real world is messier than AI's training data, self-driving took 20 years and $170B 0:55 From Stanford to a16z to Grepr 7:00 Why observability spend stopped paying off 11:00 From autocomplete to AI that trains engineers 13:10 Why AI widens the engineer skill gap 14:55 Syntax vs semantics 18:00 Inside Jad's AI workflow 22:55 Six months of work in 10 days 24:50 Can AI reason about how systems really run? 30:10 Why AI SRE tools start in the wrong place 32:30 Can AI run a distributed cluster yet? 37:35 Why engineers buy extra capacity over predicting 39:15 How AI could trace failures in production 43:45 Why AI for ops is harder than for code 49:55 Can AI do a VC's job? 52:05 Why self-driving took 20 years and $170B 57:05 Customers skipped the AI pitch for the 90% savings YouTube: @jadtnaous @grepr_ai @martin_casado

Engineering T cells for immunotherapy vs cancer had already had marked success in certain types. But there's a next generation in the hopper, as reviewed today @Cancer_Cell, that ought to help take the field much further into solid tumors

ENGINEERING WONDER 🇨🇳 Details on Tesla’s new TL2 front motor in the Shanghai-built Model Y Performance are finally coming into focus, and anyone expecting a routine refresh of the familiar 3D3 is in for a big surprise 🔥 Production and regulatory plates reveal that the new Performance model pairs this fresh TL2 front motor with the proven 4D2 rear unit, delivering an output split of 176 kW front and 291 kW rear 🆒 The real story, however, isn’t just that the TL2 makes more power than the outgoing 3D3—it’s where those gains actually show up: ⚙️ Torque: Up only ~9% (219 Nm to 238 Nm) ⚡️ Power: Up nearly 29% (137 kW to 176 kW) 🛞 Redline: An extra 2,000 rpm (17,000 rpm to 19,000 rpm) That contrast tells us a lot about the motor's real-world intent 👀 Rather than chasing a massive spike in off-the-line launch torque, Tesla engineered the TL2 to carry its power much deeper into the rev band. That translates directly into stronger pull at passing speeds and significantly better top-end stamina where earlier dual-motor setups typically began to taper off 😎 All told, the specs point to an unmistakable conclusion: the TL2 is not a rebadged 3D3 with a minor software tune. Jumping from 137 kW to 176 kW while unlocking another 2,000 rpm confirms that the refreshed Model Y Performance is packing a genuinely overhauled front drive unit 💥

engineering is moving 10x faster. design review hasn't caught up. introducing Impeccable ..for PRs! a design review agent that simulates your users, uses your app, finds friction & slop. Impeccable’s design judgment in every PR. reply if you want in on the closed beta 👀