hamiltont

Once you hit the memory you need, generation speed is mainly set by bandwidth, and every Ultra from M1 thru M3 has ~800 GB/s. IMO best ROI for most people is 'cheapest used Ultra with enough RAM'

I setup an eBay alert and picked up a used M2 Ultra that has delivered good ROI (at least, far better than 15k for comparable-for-my-use-case performance)

> We're not taking funds from customers yet — we're personally paying for all the provider requests during this phase. Credit purchases are disabled.

This appears on their credit purchase page right now, but you have to email them to get credits (everyone starts with zero)

Not sure I'm fully following your question, but maybe this helps:

IME deep thinking hgas moved from upfront architecture to post-prototype analysis.

Pre-LLM: Think hard → design carefully → write deterministic code → minor debugging

With LLMs: Prototype fast → evaluate failures → think hard about prompts/task decomposition → iterate

When your system logic is probabilistic, you can't fully architect in advance—you need empirical feedback. So I spend most time analyzing failure cases: "this prompt generated X which failed because Y, how do I clarify requirements?" Often I use an LLM to help debug the LLM.

The shift: from "design away problems" to "evaluate into solutions."

Anecdotal tip on LLM-as-judge scoring - Skip the 1-10 scale, use boolean criteria instead, then weight manually e.g.

- Did it cite the 30-day return policy? Y/N - Tone professional and empathetic? Y/N - Offered clear next steps? Y/N

Then: 0.5 * accuracy + 0.3 * tone + 0.2 * next_steps

Why: Reduces volatility of responses while still maintaining creativeness (temperature) needed for good intuition

ok, will do

Hi - I'm working on something like this because I needed it too ;-)

It's not yet ready for release, but I should be ready for beta-test within 2 months. If you're interested I would be happy to add you to my list of "people to notify when I am ready to beta test"

We adopted Amazon's 6-pager strategy for meetings. It has been incredible helpful. TLDR - one person writes a document explaining the proposed feature, max length 6 pages. That one person does 80% of the work (whatever your org wants e.g. timeline, target customers, planned KPIs and/or ROI).

Process: Start the meeting, turn on your camera (surprisingly important for us), and set a timer. Everyone quietly reads for ~20min. AFTER they finish reading, restart and engage via comments - positive/agreement comments are great, questions are useful, answering/responding to other's questions is encouraged. Post 20min, one speaker goes through the comments. Many (70% is common for us) will already have been resolved or will not require discussion. Long comment chains or lack of consensus on a comment chain is where we spend the discussion time. Common pitfall - Speaker DOES NOT present the document, that just wastes 20min and bores everyone to death. Small nuance - speaker (doc preparer) is already familiar with doc, so they watch the timer. They also check in e.g. at 15 give a 5min warning, at 20min check if anyone needs more time, etc.

Huge net positive for us: - No more "pre-read" (no one does it), we just say that a 45min meeting is when you BOTH get the information AND discuss is - Post the "20min" we are already 80% on the same page - Far far fewer "basic" questions that kill time - those were answered by the doc - Quiet reading gives people time to mentally switch/shift from their last 5 meetings and come up to speed. Less "anxious energy" when talking - Increase collaboration - "Type A" talkers cannot (as easily) dominate the comments. Leaves "airshare" for quieter team members to participate via comments. We've begun encouraging all to leave at least 4-5 comments

Negatives: - REQUIRES senior team member support, else 1 senior "ass" will break the rules and start talking - Writing the document is time consuming and not easy. This really becomes one persons "work" to gather the data from all stakeholders in a 1-1 fashion, organize it, summarize it

Anyone else wanting to see the original content of /unicorn? Cats are great and all, but we want unicorns!!

<3 wonderful project. Brings back memories of excitedly writing HTML in my drawing notebook and daydreaming what the pages would look like

> ...these are small enough to enter the body’s cells and tissues

Same. No training to identify concern vs FUD in health news, but I'm erring on side of caution. We are limiting exposure to plastic drink bottles in general (sport drinks in plastic during/after children sporting events are common where I live)

My ad experience has changed dramatically after I leaned into multiple browser personas. In personal persona, I hate ads with passion and do everything to get rid of them (browser extensions, premium membership, etc). I value my personal time and the ads are totally useless.

In work persona, I suddenly have found ads are actually useful. Often find myself choosing to spend 30 seconds watching a YouTube ad because it is relevant to topics I need to be aware of as a CTO. It's clear my daily browsing history influences the ads I am seeing, and I see useful information. Been looking into SIEM tools lately, and via an ad I was just made aware of some data center appliances for security. I clicked to their website and browsed a while to learn what was available. When you have some real challenges to solve and the targeting is on point, ads can be a great news feed.

Clearly segmenting my browser history into one persona where I am actively looking for solutions vs my personal persona where I want to be left alone helped the feeds target me.

Still, surreal feeling to intentionally choose to watch an ad...