u/zhuu_uq

I'm searching for some kind of new lighting solution for my study desk.

Im pretty old-school when it comes to studying. I retain things way better when I handwritr my notes and ideas, so for a long time I've relied on pen and paper. The problem is that I then have to spend extra time turning everytthing into digital notes for assignments and presentations.

Over the past few months, I've been trying to do more of my studying and writing directly on my MacBook. I'm finally getting used to it, but there's one thing I haven't figured out: lighting. I've still been using the same basic desk lamp I used for handwriting, and after a long computer session, my eyes feel really sore. It's starting to affect how well I can study.

So what do you guys use for studying on a computer? Are traditional desk lamps just not that great for screen-based studying?

With monitor light bars, screen lamps, indirect lighting, etc., I'm honestly not sure what I should be searching for.

Planning 9 nights in Italy for 6 people:

2 kids, 7 and 11 2 parents 2 grandparents in their late 60s

Kids want pizza, gelato and “something fun.” Parents want Rome + Florence and to actually see things.

Grandparents are fine walking, but not 18k steps, stairs all day and a new hotel every 2 nights.

Also need 2 rooms, an elevator, breakfast and somewhere everyone can get back to easily for an afternoon break.

My first itinerary was basically adult sightseeing with children attached.

Rebuilt it on Zenvoya with actual rules: ● one main thing each morning ● long lunch / hotel break ● optional evening plan ● max 2 hotel bases ● no crossing the whole city for one attraction Way better.

The Rome days especially stopped looking like Vatican → Colosseum → Trevi → family collapse.

Now it groups nearby stuff, leaves actual recovery time and gives us an optional plan if the kids still have energy.

Turns out family-trip planning is less “what should we see?” and more “who is going to be miserable by 4pm?”

What rule saved your multigenerational trip?

had an eval that looked completely fine from the outside.

user: move Sarah's appointment to Friday at 3

agent: Done. Sarah's appointment has been moved to Friday at 3 PM.

LLM judge gave the response a strong score. relevant. concise. followed instruction. no hallucination in the final wording. then looked at the trace.

the agent had called: reschedule_appointment(customer_id=1842, date=...) Sarah was customer_id=1482. valid tool. valid schema. valid date. wrong fucking person. this is where I'm starting to think we ask LLM judges to grade way too much. there are things they're genuinely useful for:

● was the answer relevant? ● was it complete? ● was the tone appropriate? ● did it understand what the user was trying to do? ● did the conversation become confusing?

but if my system already knows the expected customer ID, why am I asking another model whether the tool call “seems correct”? just compare the IDs.

same for: ● tool selected ● amount ● date/timezone ● permission ● required confirmation ● backend state after action ● whether escalation happened ● whether the API actually succeeded those should be boring assertions wherever possible.

so I'm moving toward: probabilistic evals for subjective behaviour ● deterministic assertions for business facts LangSmith/Langfuse/Phoenix are still useful because I absolutely want the trace when something fails. but tracing tells me what happened. I still need a regression set actively trying to make it happen again before the next release. TestMu Agent Testing is interesting here because it runs scenario sets against the actual agent endpoint and evaluates the conversation + expected behaviour/tool actions across runs instead of only grading the last message. doesn't magically solve evaluator disagreement obviously. you still have to decide what gets judged vs what gets hard-asserted.

I'm just increasingly uncomfortable with: LLM does thing → LLM grades thing → dashboard says 94% → ship what parts of your agent evals do you still let an LLM judge score, and what have you moved to hard assertions?

my nifty options algo has finally moved past cute test quantity and now one big order feels stupid.

options rn:

•fixed manual chunks •price-aware slicing •iceberg

nubra has proper iceberg controls where i can set max visible qty or number of slices. much cleaner than maintaining another homemade loop.

but does iceberg actually help execution or just hide size while the same bad limit price sits there?

how are you choosing slice size? order-book depth, average volume or pure vibes?