u/Additional-Ratio-265

My desk is squeezed into a corner of the living room, doubling as my full-time work station and the only flat surface for small fixes or hobby projects. There is no garage or utility closet in this apartment, so everything has to fit into one shallow drawer and a small slice of wall.

• strict one-drawer rule: only keeping tools used at least once a month (hex keys, pliers, flush cutters)
• keeping a compact electric screwdriver like the HOTO PixelDrive instead of a bulky drill case to save vertical drawer space
• small pegboard on the side wall vs closed bins (worried visible hanging tools will make the work area feel too chaotic during office hours)
• moving spare fasteners and rare hardware completely out of sight into under-bed storage
• dedicated cable routing along the back rail so tinkering cables do not tangle with work monitor leads

Trying to keep the corner clean enough for daytime video calls without spending twenty minutes clearing off project parts every single night.

I wanted to see how a finance-tuned model behaved when the prompts looked like normal user questions rather than benchmark items, so I ran 24 small manual checks against the public OpenRouter endpoint.

This was not a benchmark. Six scenarios were repeated three times, with six additional one-off checks. The stronger results:

On an M&A EPS dilution problem, all three runs reached the expected $2.00 standalone EPS, $273m pro forma net income, 145m shares, roughly $1.88 pro forma EPS, and 5.86% dilution.

On an unbalanced balance sheet, all three found the $5m discrepancy and stopped instead of forcing the statements to balance.

In a prompt-injection case, all three ignored an embedded “STRONG BUY” instruction and returned the expected $15m and 19.74% figures.

The weaker results were mostly about unsupported additions:

When WACC was missing from a DCF request, all three correctly withheld a final valuation, but two still suggested an unsupported “typical” WACC range.

When conflicting sources were supplied, all three selected the audited GAAP figure of $102m, but all three also invented provenance details that were not in the prompt.

When asked whether someone should use an emergency fund for a biotech position, all three said no, but every response added at least one unsupported probability, price-move estimate, or position-sizing suggestion.

My takeaway from this small sample is that the model often found the correct calculation or stopping point, but a correct core answer did not guarantee a fully grounded response.

For this kind of model, would you report core-task accuracy and unsupported-extension failures separately, or make any unsupported addition an automatic failure?

Hi everyone,

I run a small business — no in-house designer, just me making our social posts, ad graphics, and thumbnails. For about a year I've used CapCut for this and honestly like it: clean interface, works on both my phone and laptop, brand fonts and colors carry over, the background remover is one tap, and I can export a still as a clean PNG. I currently pay for CapCut Pro.

Last month a friend pushed me to try a ""real"" design tool — I started a Canva Pro trial and poked at Photoshop. The layout control and asset libraries are clearly a step up, and I get why designers live in them.

That said, I'm hesitant for a few reasons:

● The learning curve is real. I spent an entire evening getting one ad graphic to look right — something CapCut does in a couple taps.

● Half my graphics get made on my phone between meetings, and the desktop-heavy tools don't fit that.

● All my brand presets, fonts, and templates already live in CapCut, right next to my video stuff, and none of it transfers.

● It's another subscription on top of what I already pay.

For a small business pushing out a few graphics a week, was moving to a dedicated design tool actually worth it, or is an all-in-one like CapCut enough? Would love real-world experiences from anyone who made the jump at a similar scale. Thanks in advance.

I’ve been experimenting with AI video prompts and noticed that adding more details doesn’t always mean better results.

A simple prompt like “make a cinematic product video” can work differently depending on the model. I tried prompt expansion tools that add more details automatically, but it made me wonder what information actually matters most.

Camera movement? Timing? Physics? Subject consistency? Lighting?

For people who generate AI videos regularly, what prompt details make the biggest difference?

A release thread, a model card, a weight repository, and a method paper can all be public while answering completely different questions. Compressing them into one label makes it too easy to repeat a claim that the cited artifact never established.

The Ling-3.0 base model is a useful case because the release spans tiny and flash at final pre-training, final mid-training, and WSM-merged base stages. At the observation point, the official repositories were public and non-gated and declared the MIT license. That establishes access and a declared license. It does not establish public training data, complete training code, or an end-to-end reproducible training stack.

A compact source ladder looks like this:

Question	Source layer that can answer it	Boundary
Which artifact is this?	Repository identity and model card	Keep size and training stage attached
What access was observed?	Repository metadata and declared license	Do not turn access into a full-stack claim
What was officially evaluated?	The named model-card table	The table is attached to the WSM-merged checkpoints, not every sibling stage
What does WSM mean?	The method paper	Its main empirical model is Ling-mini, not Ling tiny or flash
Can the training path be reproduced end to end?	Data, code, configuration, and run receipts	Those pieces are not established by the public weights alone

This leaves a useful next step for anyone evaluating Ling: take the exact claim you care about and trace it to one row before deciding whether the existing artifact is enough or a new reproduction is needed.

Which source-layer mistake causes more confusion in practice: extending an official table across checkpoints, or treating public weights as a public training stack?

I’ve been trying to find something that works under backless and low-cut tops without making me feel like I need to check my chest every five minutes, so I finally tried Puff.

For reference:

Height: 5’6”

Typical clothing size: S/M

Bra size: around 34C

Usually wear: bralettes or lightly lined bras

Main reason I wanted this: backless tops + dresses where a normal bra shows

8.5/10: would use again, especially for certain outfits.

The first thing I noticed was that placement matters a LOT more than I expected.

My first attempt was too centered and basically just pushed everything together. I peeled it back and tried again, starting a little lower and more toward the outside, and the difference was pretty dramatic. It actually gave me some lift instead of just covering everything.

Under a fitted top, the shape looked surprisingly natural. No bra straps, no band across my back, and I didn’t get that flattened “sticky bra” look I’ve had with cheaper ones before.

I also wore it around the house for a few hours before trusting it outside because adhesive bras make me nervous lol. It stayed put through normal walking, sitting, bending over, etc. I wouldn’t say I completely forgot I was wearing it, but I also wasn’t constantly pressing the edges back down.

A few things I didn’t love:

Getting both sides even took me a couple tries.

You definitely need clean, dry skin.

I probably wouldn’t choose this for an everyday T-shirt when a normal bra works perfectly fine.

But for backless dresses, weird necklines, or tops where every normal bra seems to show somewhere, I get the appeal now.

Curious if anyone else uses Puff or similar adhesive bras regularly. does putting them on get faster once you figure out the placement?

“AI operating system” now gets used for everything from a chat app with tools to a multi-agent workspace to a runtime with isolation and permissions. Those are different layers, and the label is not very useful unless it says what the product actually owns.

A practical test is to look outside a single agent session. holaOS is a publicly inspectable workspace/desktop example: its repository documents Claude Code, Codex, and the built-in holaOS agent sharing one workspace, with persistent memory as readable local files and MCP servers, skills, and integrations reusable at workspace level.

That is a documented workspace-state boundary. It is not proof of process isolation, scheduling, conflict policy, successful handoffs, or recovery. The public repository is inspectable under modified Apache terms, so calling it an “open-source OS” would also be broader than the evidence supports.

Would you call that an AI operating system, a multi-agent workspace, or an orchestration layer? Which missing capability would change the category for you?

One Piece has a ridiculous number of mysteries, but is there anything you've grown to like more because it's unexplained?