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#Cybersecurity#Privacy#ChromeExtension
Hot damn, this is some massive drama… Factory AI board member Chris Degnan accused for secretly giving out confential business details to Cognition; terminated from Factory; 50 minutes later announces joining Cognition as CRO… WTH 🤯
https://t.co/fBbk3jQ2P4
Heard a software engineer say: "my cofounder is Claude" about a project they built + shipped.
I am baffled by people humanizing AI, given it's just a tool. The same way my keyboard is not my cofounder (though I could not write @Pragmatic_Eng without it!) but a means to an end
Scrum made sense at companies/teams that shipped a new version of their product every 2-3 months (or less frequent)
As soon as a team got to shipping daily or had CD: they only held teams back
Most startups + Big Tech moved on a decade+ ago, legacy companies remained only
When you look for ways to make agents be more efficient at building good, quality, useful software: you start to discover (or re-discover) software architecture and programming practices that are decades old
Shallow vs deep modules
Tracer bullets
The surgeon team
25+ year old books suddenly become very useful to work better with these AI coding agents
How ironic is this?
(This thought comes after we talked about just this with @mattpocockuk. It will be a podcast episode)
Two things here:
1. Know that fake job interviews set up to have you install malware on your machine are becoming REALLY common. It's obscene.
IDK what the solution is: be viligant, and if absolutely necessary to run sg, use a clean VM, isolated from everything? Be paranoid
Food for thought that extremely experienced engineers with 30+ years under their belt who could easily retire are starting new companies to push the boundaries of AI
And know tech folks who have semi-retired getting back into the game
BUT also heard from a few (very few!) devs leaving the industry, in fear of what AI will do to it
HypothesisForge: open-source, autonomous ML-research agents that hypothesize, run a real GPT-2 experiment, critique the stats, and write the report. Apache 2.0. Follow us for more.
https://t.co/svPYm2q3WB
Wild to see every VC funded and publicly traded tech company rebrand to being an AI company
Paging company —> the platform for AI-first operations
CDN —> AI Cloud
Helpdesk software —> AI-powered helpdesk platform
o11y —> AI-powered intelligent o11y
Everywhere…
If you have ever thought/argued about “productivity” but not read the book The Goal by by Eliyahu M. Goldratt: read it!
It’s about a factory where everyone is measurably 100% productive, headed to bankruptcy.
The factory is turned around by… not focusing on productivity!
This has been an open secret for at least 18-24 months. GH starts have been heavily purchased by many projects (not all!) that tried to show traction, and drum up VC investment.
Better VCs have had custom tools to rank organic vs paid GH stars 18 months back, easily...
Founders + C-level folks who come from a software engineering background are all getting back to it.
Never seen eng leaders get hands-on with a previous technology at so many different places.
This is either brilliant or scary:
Anthropic accidentally leaked the TS source code of Claude Code (which is closed source). Repos sharing the source are taken down with DMCA.
BUT this repo rewrote the code using Python, and so it violates no copyright & cannot be taken down!
- Predictive Precision: Reasoning capability can be derived from the model’s learned dynamics, providing a self-contained metric for comprehension.
It's time to treat LLMs as complex physical systems rather than just statistical parrots.
The Paradigm Shift:
- Beyond Benchmarks: We move from "black-box" testing to internal state measurement.
- Self-Organized Criticality: We identify the specific physical phase where reasoning manifests.
Our new research demonstrates that PLDR-LLMs exhibit Self-Organized Criticality (SOC). This isn't just a theoretical curiosity; it allows us to quantify reasoning with high precision by analyzing the metastable steady state of the model itself.
This is more common than you'd imagine at large layoffs. Happened eg at Uber's 20% layoffs in 2020 as well.
It is because layoffs are decided at Director-or-above levels, and quotas need to be hit. Directors (or above) don't have all context, and some key folks are let go. Oops!