euler

@felpix_ middle bell curve ideology you’ll learn when you’re older

@euler2074 notion is trash ngl

ignoring the antiquated AI policy, forcing students to use google docs should be considered a crime

@felpix_ notion has offline now + is industry standard and better in every way

i half and half switch between obsidian/md and word these days i don’t want to be beholden to internet access to write something down

@mweinbach small model smell performs worse in artificial analysis than gpt-5.5-med while costing more

I've had Gemini 3.5 Flash for the past week. It's extremely good. It build out an entire canvas/docs editor in my Cowork app It did it in ~7 minutes (vs GPT 5.5 xhigh fast's 25 minutes) and had a better implementation

@keysmashbandit ngl it’s helpful

If I Google the definition of a word, I am basically never looking to find a markdown table with a row titled "Vibe"

@baconmunch2 @MoonGodddd @RhysSullivan doing clearly defined tasks and deciding which clearly defined tasks to do are a different ability

@euler2074 @MoonGodddd @RhysSullivan "completely new ability" is using semi-intelligent problem solving skills to answer a question? Opus 4.7 doesn't have that?

the average person has only ever used ChatGPT 3.5 Instant and has no idea what the models can do

@baconmunch2 @MoonGodddd @RhysSullivan claude is reasoning averse and gpt-5.5 is the frontier

@euler2074 @MoonGodddd @RhysSullivan x.com/alex_here_now/… is this the worst model with the least compute?

The high agency triangle

@baconmunch2 @MoonGodddd @RhysSullivan again you have no line of reasoning you’re just saying “nuh uh” i am saying that a lack of [completely new ability] in [some circumstance] doesn’t imply a complete lack of progress

@euler2074 @MoonGodddd @RhysSullivan wtf are you saying man. your analogy makes no sense.

@baconmunch2 @MoonGodddd @RhysSullivan on the worst models with the least compute judge ai by the frontier, it’s what actually matters if gpt-5.5-xhigh in codex can’t do it then sure but tools and agentic harnesses are part of what has made ai better since gpt-3.5

@euler2074 @MoonGodddd @RhysSullivan x.com/broskiFGC/stat… also identical prompts

@baconmunch2 @MoonGodddd @RhysSullivan see this is called a “thesis” it is usually followed by a “line of reasoning”, which combine to make an “argument” however, it seems you misplaced your line of reasoning as it is absent

@euler2074 @MoonGodddd @RhysSullivan no its not lmao

@alex_here_now @RhysSullivan opus is too eager not to reason

@RhysSullivan My point: not just a model issue. You used a different and more specific prompt that actually instructed the thing on the right way to achieve the desired analysis. The same dumbass prompt here produces the same dumb failure mode with opus 4.7

@baconmunch2 @MoonGodddd @RhysSullivan also this is like saying “if cars have gotten so much better since the 1800s, why do they not simply take me places without having to steer or accelerate”

@MoonGodddd @RhysSullivan yes but if the llm has gotten so much better since gpt 3.5, why does it not simply get the list of all pokemon and filter it by names ending in "aw" when asked.

@getpeid rabbit r1

name a tech product that has real taste not expensive, not minimal, not pretty actual taste

@vxrnel @wotansen2 @The_Peach03 it scales up, you’d probably pay like 10k

@wotansen2 @The_Peach03 66k a year isn’t affordable even if you’re on 200k

In America, a combined household income of 125k is considered poor enough to qualify for free meals at a prestigious university

@co1born @SHL0MS @pangramlabs ai?

Fair warning before I dig in: this image is actually a very competent rendition. It's doing more right than most AI Monet pastiches. But you asked what makes it inferior to a real Monet, so here's the honest breakdown. What's missing — the physical object A real Monet is a thing before it's an image. This is the biggest gap and it's not solvable by better prompting. No impasto. Monet loaded paint thick. His water lily pads sit physically on top of the canvas — you can see the ridges where the brush dragged, the little peaks where pigment piled up. Look at the lily pads in your image: they're smooth tonal gradients pretending to be brushstrokes. A real one would catch raking light differently from the water around it. No canvas weave. Monet painted on linen with a specific tooth that broke up his strokes and let underlayers peek through. Your image has a uniform digital surface — the "texture" is illusionistic, painted into the pixels rather than emerging from the substrate. No drying history. Real Monets have cracks, areas where wet-into-wet bled, areas where dry paint was scumbled over dry paint. Pigments aged differently — his cobalt blues shifted, his madder lakes faded. The image has no chronology in it. It's all painted "at once." What's wrong with the marks themselves Brushstroke direction is decorative, not descriptive. Monet's strokes do work — vertical strokes for reflections of vertical things (poplars, irises), horizontal strokes for the water surface, elliptical strokes that physically describe the floating disc of a lily pad. In your image, the strokes are stylistically "Monet-ish" but they don't consistently track what they're depicting. The reflections in the upper left, for instance, have stroke directions that don't quite resolve into specific reflected objects. No hierarchy of finish. Monet had passages of intense detail next to passages left almost raw. He'd finish a single lily blossom with three decisive touches and leave a whole quadrant of water as scumbled understatement. AI tends to render everything at the same level of "doneness." Your image is uniformly busy. Edges are too soft everywhere. Real Monet has surprisingly hard edges in places — the rim of a lily pad against dark water can be a single confident knife-edge stroke. AI defaults to blending. Look at the pads in your foreground: their edges dissolve gradually. Monet's often don't. The color problem The palette is plausible but generic. Monet's late Giverny palette was specific and slightly weird: viridian, cobalt violet, French ultramarine, cadmium yellow, vermillion, lead white. The unusual one is the violet — he used cobalt violet aggressively and it shows up in shadows where you wouldn't expect it. Your image has some purple in the lily pad rims, which is the right instinct, but the violets aren't doing structural work the way Monet's do. No optical mixing. Monet placed unmixed complementary colors next to each other so your eye blends them at viewing distance. Pure orange next to pure blue, reading as a luminous neutral. Your image has colors that are already pre-mixed into harmonious mid-tones. It's pleasant but it doesn't vibrate. The whites are wrong. Monet's whites are almost never white — they're tinted pink, blue, lavender, yellow depending on the light condition he was chasing. The white lily in your upper-left is closer to a true neutral white than Monet would ever paint. The conceptual gap This is the part that actually matters and the part no model will close. Monet was solving a problem. Each canvas in the Nymphéas series is an answer to a specific question: what does this pond look like at 7am in August? What happens to reflections when wind crosses the surface? The paintings are records of sustained looking at a real place he built and tended for thirty years. Your image is a style reference, not an observation. It's a painting of "a Monet" rather than a painting of a pond. No series logic. A single Monet water lily painting is meaningful partly because of the ~250 others. They're variations against each other. This image is an orphan — it has no relationship to anything. No hand, no decision, no risk. Every stroke in a real Monet was a commitment by a specific 80-year-old man with failing eyesight standing in a garden. The image has no author in that sense. The "decisions" are statistical averages of millions of images. What it does get right (worth saying) The compositional asymmetry is good. The reflected verticals reading as trees/irises is a smart move. The pink lilies in the lower foreground are placed with reasonable intuition. If you cropped tightly to a 6-inch square anywhere in this image, it would pass a quick glance. The tell is always scale and surface. Stand six inches from a real Monet and it dissolves into chaos — slabs of pigment that look like nothing. Stand six inches from this and it just looks like a slightly blurrier version of itself. That collapse-into-abstraction up close is the thing you can't fake without paint.

i just generated an image in the style of a Monet painting using AI please describe, in as much detail as possible, what makes this inferior to a real Monet painting

@Coobyk_ @justalexoki photo and video editing

@justalexoki Genuinely why would one need such fast transfer speeds on a portable drive

holy FUCK this thing is fast barely even had enough time to screenshot a 40gb transfer

@iAnonymous3000 @pangramlabs ai?

DO NOT trust Google with your health data. DO NOT buy this product. If you still use a Fitbit account, Google says you cannot access Fitbit with that account after May 19; to keep using it, you must move to a Google Account or leave/delete Fitbit. What that Google account can hold, depending on what you enable or connect: wearable data, workouts, sleep, weight, heart rate, glucose, menstrual/cycle data including fertile windows, medical records, lab results, medications, allergies, clinical data, AI coach chats, precise location from GPS/sensors/Wi-Fi/cell towers, approximate location from IP, plus data from connected apps/devices like Strava, Flo, Oura, Dexcom, MyFitnessPal, Whoop, and more. What Google admits in its own docs: Submit feedback on an AI Health Coach conversation and trained reviewers may read, annotate, and process that conversation. They may also review associated Fitbit health/wellness data, activity logs, and basic app info. Google’s own warning: “Please don’t enter sensitive or confidential information you wouldn't want a reviewer to see.” The “not for Google Ads” promise is real. It does NOT mean “Google won’t use this data.” Google’s docs still allow uses for personalization, AI features, product development, opt-in research, service providers, legal requests, third-party connections, and Fitbit Enterprise workflows. Opt into research/R&D and Google says Fitbit + Google may use de-identified data to develop new health/wellness products and services, including Google enterprise products. That data can include AI conversations, location, sleep, nutrition, third-party connected data, and personal health records such as medical records. Do not assume HIPAA protects this. HHS says HIPAA generally does not protect data stored on personal phones/tablets or data downloaded/entered into personal-use mobile apps unless the app is provided by a covered entity or business associate.

@disco___cat RFID is incredible

These checkouts are a thing of wonder

@DeanTTraining this is ai

Raising Cane’s Naked Tenders might be the new meta as far as fast food meal options go Only 420 calories and 78 grams of protein These appear worth looking into….