u/Balance-

GPT-6 Luna is priced at $0.10 per million input tokens and $0.50 per million output tokens. Claude's cheapest model, Haiku 4.5, costs $1 and $5. That's ten times more.

Agents send most of their calls to small, fast worker models. Those calls are where most tokens get spent, and a 10x gap on that much volume decides which vendor gets picked. Caching maintains that 10x gap: Luna charges $0.01 per million for cache reads, Haiku charges $0.10.

Anthropic has been cutting prices higher up. Opus 5.5 is $4/$20, down from $5/$25 for Opus 5. Haiku is the tier that hasn't moved. Haiku 5.5 is due in the coming weeks, but there's no price yet.

A better Haiku at $1/$5 won't fix this. Anthropic needs something close to Luna's price, whether that's Haiku 5.5 or a new tier below it.

I wanted AV2 streams to test decoders with, and the only official conformance streams I found are commercial. So I encoded some myself with AVM, the AV2 reference encoder, using clips from the official AV2 test set.

Available now: three low-resolution clips (270p, a vertical 270x480, and 360p), each at three quality levels (QP 110, 160 and 210). Each stream comes with an MD5 of its decoded output from the reference decoder, so you can check whether another decoder produces the same frames.

Caveats:

• These aren't official conformance streams. AV2 is still changing, so they're tied to the AVM commit they were made with.
• They were built on Windows without TensorFlow Lite, which means two encoder experiments were off. Details are in the README.
• 720p and 1080p are coming. AVM currently crashes on those resolutions in my Windows build, but working on it.

The Python script that produces everything is included. It builds AVM, downloads only the needed frames of each source clip, and encodes in parallel within a memory budget.

Link: https://github.com/EwoutH/AV2/tree/main/samples

If you try them with dav2d or another decoder, I'd be interested to hear whether the MD5s match.

LOFIC (Lateral OverFlow Integration Capacitor) is a pixel design that lets image sensors handle far more light without sacrificing shadow quality. Each pixel gets two charge "buckets": a small one with high conversion gain, which produces a strong, clean signal from few photoelectrons, and a larger one that catches any overflow once the small bucket fills. At readout, the sensor first measures the small bucket alone for clean low-light detail, then combines both buckets to measure bright highlights. It uses whichever reading suits the amount of light captured.

Unlike older dual conversion gain sensors, it doesn't require choosing a gain mode in advance, and the concept could extend beyond two buckets. The approach traces back to an early "cascaded integration" patent by CMOS inventor Eric Fossum and was later refined by researchers at Tohoku University.

It seems to use a 96-bit LPDDR5X memory bus, instead of the previous 64-bit wide busses. Considering it's on 2nm, that's expensive silicon. That should result in around 115 GB/s memory bandwidth.

A20 Pro also doubles the size of Apple's dedicated Neural Engine (from 16 to 32 cores total).

Does anyone know of publicly available pre-encoded AV2 samples at 1080p or 4K?

I’m looking to benchmark dav2d decoding performance, so ideally something multi-frame / several seconds long rather than tiny conformance streams or single-frame tests.

IVF, MP4, or raw AV2 bitstreams are all fine. Preferably 10-bit 4:2:0, but anything 1080p+ would help.

Links appreciated!

I ranted about this half a year ago, so I checked Lenovo’s current 2026 lineup to see if things had improved.

Mostly, no.

Lenovo seems to offer a bit more 100% sRGB IPS and OLED upgrades, but **45% NTSC is still the base display tier across a ridiculous range of laptops**, including current configurations of:

- IdeaPad Slim 3 / Slim 5
- IdeaPad 5 2-in-1
- Yoga 7 2-in-1
- ThinkBook 14 / 16 G9
- ThinkPad P14s, P16s, P16v
- Lenovo V-series
- multiple Chromebooks

For context, 45% NTSC is roughly **62–65% sRGB coverage** in practice. Colors look visibly dull and washed out compared with a proper 100% sRGB display.

What makes this worse is that Lenovo clearly knows better. A lot of these models now have the same structure:

**Base:** 45% NTSC IPS
**Upgrade:** 100% sRGB IPS
**Premium:** OLED / wide gamut

So this isn’t old inventory. It looks like an intentional cheap-panel tier Lenovo keeps around for cost segmentation.

And that’s (somewhat) understandable on a $400 IdeaPad.

It’s much harder to excuse on a **Yoga 7** or a **ThinkPad P-series workstation**.

Meanwhile, Apple is putting an 100% sRGB-class 500-nit display in the $599 MacBook Neo.

Maybe a more accurate summary is: **Lenovo doesn’t put 45% NTSC in literally everything. It just refuses to stop offering it in almost every product category.**

It’s almost like there is somebody at Lenovo who gets an alert whenever a base-model laptop accidentally reaches 100% sRGB.

- Previous post: https://www.reddit.com/r/Lenovo/s/pplrgO6KYo

Just encountered this limit on Plus. Makes no sense.

The issue isn’t that I need five things running constantly. Some scheduled tasks might run once a week, once a month, or only check for something occasionally. Yet a monthly reminder apparently consumes the same scarce slot as a daily recurring task.

That makes the feature weirdly self-defeating: the more useful you find scheduled tasks, the faster you hit an arbitrary ceiling.

Limiting users to five active task definitions total, regardless of how rarely they run, feels like the wrong constraint.

At minimum, infrequent tasks should either count differently or the cap should be substantially higher.

Maybe 5 daily? Where a monthly tasks counts as 1/30th of a daily task?

Not a transformer. It's a recurrent latent reasoning setup that keeps "thinking" in latent space before answering. Sits completely outside the published cost/accuracy frontier for ARC-AGI, and something this size runs on basically anything. Paper is from the Pathway team, dropped 4 days ago. I want to see it scaled to 1-3B before getting too excited, but the shape of the result is wild.

> De Nederlandse gasopslagen zijn voor 40 procent gevuld, terwijl het ‘vulseizoen’ al een eind op weg is. Vorig jaar was de voorraad al in juni zo ver gevuld. De doelstelling, een vulgraad rond de 80 procent, is nog ver weg. En bijvullen is met de hoge gasprijzen een kostbare zaak.

I’m curious, are people using OpenAI’s Prism? How do you experience it? Are you using it in your regular work? Is it still being continued to be developed?