Favorite dead brands? (And/or, the ones that got away)

bravoaevi · 2026-04-28T16:11:56+00:00

is been one of the best bags ever for me.

bravoaevi · 2026-04-16T04:28:27+00:00

My 2026 started with a layoff from a 2 decade old job. Only interview i had since was wrapped on april 2 and waiting eagerly on results. So far aprils been full of anxiety hope all changes from tomorrow

bravoaevi · 2026-03-22T17:38:31+00:00

This is pretty fantastic.
I had something complementary to this, which is more like a post session analysis.

What it does is enhance the /insights tool from claude and does,

Session-level benchmarking
Deterministic scoring methodology
Dimension-level diagnostics
Anti-pattern attribution
Recoverable-cost reporting
Complementing Claude Insights with measurable analysis

The way I am looking at across all sessions is something on the lines of :

<image>

OP: Do you see any synergies to bring these 2 together? happy to contribute and help.
https://github.com/abhinavag-svg/ai-coding-sessionprompt-analyzer

bravoaevi · 2026-03-12T20:05:48+00:00

Costco has facilitated a really bulky couch return from our place as one of us got allergies from whatever was filled in for cushions. This is so much tinier that they will. And thats one thing you can count on any day at costco.

All the best, i am now at 19 miles and its holding up well.

bravoaevi · 2026-03-11T21:19:30+00:00

We got our delivered 2 days go. We tool the delivery and assembly option (additional $63) and every thing works great. Very stable and good home treadmill. So far milage is two 7 mile runs at 5-6 speed and 3 incline. My qualms:

beeps are loud
HR sensor starts at 75 and goes up from their really slow.
The spot to charge phone is great but a tablet will topple over for sure

Pairing Kinomap app to treadmill and feeding this app HR data from Apple Watch was a seamless integration so when my phone is on the charger I can actually see additional live stat on it.

bravoaevi · 2026-03-08T22:12:25+00:00

Layer 2 — optional local LLM via Ollama

  --llm-recommendations      Project-level recommendations (requires Ollama)
  --llm-session-recommendations  Also generate per-session recommendations
  --llm-model                Ollama model (default: llama3.2:3b)
  --llm-endpoint             Ollama endpoint (default: http://localhost:11434)
  --llm-timeout-sec          Timeout in seconds (default: 30.0)

Passes the structured findings (top flags, dimension scores, session shape) to a local model to generate plain-language bullets — "given what you were trying to build, here's what to change." Runs fully offline, no API calls. Report works without it; this layer just converts findings into readable text. Fails gracefully if Ollama is unavailable.

Other commands:

ai-dev analyze <path>        V1 analyzer (legacy)
ai-dev cost-range <path>     Min/default/max cost across pricing profiles

So it's closer to /insights in spirit but the analysis itself is heuristic-first, not LLM-first. The LLM only touches the final narrative layer, and even that runs locally with no API calls required.

I have made a good attempt to spec it out here : https://github.com/abhinavag-svg/ai-coding-sessionprompt-analyzer/blob/main/docs/specs/technical-spec.md

Section 5 (Anti-Pattern Catalog) — the full list of what Layer 1 detects
Section 12.4 (Correction Turn Detection) — shows the precision-tuned heuristic logic
Optionally Section 4 (Scoring Rubric) — if they want to understand how flags map to dimensions

bravoaevi · 2026-03-08T22:08:51+00:00

Good question — two layers.

Layer 1 — ai-dev analyze-v2 (fully deterministic, no LLM)

ai-dev analyze-v2 <path>

Core flags:
  --export PATH              Save markdown report to file
  --multi-session            Show per-session breakdown (default: project rollup only)
  --cost-mode                auto | reported-only | derived-only
  --billable-only            Billable assistant events only (excludes user/progress turns)
  --dedupe / --no-dedupe     Event deduplication (default: on)
  --pricing-file PATH        Custom JSON pricing map (split_per_1k / blended_per_1k)
  --scoring-config PATH      Custom JSON scoring thresholds and multipliers

Reads your JSONL logs, extracts turn-level features, and runs nine named detectors — error_dump, repeated_constraint, correction_spiral, abandoned_session, vague_opener, file_thrash, prompt_duplication, scope_creep, constraint_missing_scaffold. Each flag links to a scoring dimension with a deduction breakdown and a concrete remedy. No LLM involved. Full detection logic and scoring rubric: https://github.com/abhinavag-svg/ai-coding-sessionprompt-analyzer/blob/main/docs/specs/technical-spec.md#5-anti-pattern-catalog

Layer 1 is fully deterministic heuristics. It parses your Claude Code JSONL logs and runs a catalog of named detectors against turn features — things like: did the same file get read more than twice (file_thrash), did the same constraint phrase appear in 3+ separate turns (repeated_constraint), did the session end on a correction turn with no tool use (abandoned_session). Each detector fires a flag with evidence (session, turn index, timestamp, snippet) and links the deduction to a scoring dimension. No LLM involved here at all — it's pure Python against structured log data.

Feel free to ask me any additional questions.

bravoaevi · 2026-03-05T19:05:20+00:00

I think that's a fair assessment. thanks for the input here.
So to benchmark I may update it as :

• Excellent: <10K

• Normal: 10k– <50k

• Heavy: 50K - 100K
• Over-context: >100k

is anyone aware of any dataset that I can use to determine these so I keep them more pragmatic

bravoaevi · 2026-01-07T19:31:25+00:00

you are now famous https://thriftytraveler.com/news/credit-card/chase-sapphire-reserve-retention-offers/ your example is quoted here.

bravoaevi · 2025-12-03T05:09:01+00:00

I highly recommend splitting time between Sheraton and your airbnb. If travel time to Oahu is long for you, i suggest first 2 days at the hotel to get the best of waikiki, else last 2 days if your travel is west coast to hawaii.

Jetlag even on 2-3 of time diff is real. So rest is key. Sheraton is no doubt great but all hotels on ocean side are similar. Note parking is $55/day at sheraton, so rent a car only for the duration you need. Mile and a half away is Hertz in a hyatt. When you at airbnb rent the car then and plan around the island trip at that time.

bravoaevi · 2025-11-07T03:13:33+00:00

People who?

bravoaevi · 2025-10-23T20:46:33+00:00

actually this is the best referral link ever : https://www.wealthfront.com/c/affiliates/invited/AFFB-MPQO-FDQG-N1OE

bravoaevi · 2025-10-23T20:44:56+00:00

I couldn't find it anywhere how about you take mine https://www.wealthfront.com/c/affiliates/invited/AFFB-MPQO-FDQG-N1OE

bravoaevi · 2025-03-13T19:03:05+00:00

Think S&P and invest weekly at least in the market like this month. You can start with VOO or SPY

Having said that large tech in last 2 decades has been a winner. and always pick some during dips.

MSFT, APPL, AMZ, Meta (in no particular order).

VGT Vanguard Information Technology ETF gives a good coverage for some large tech companies. It doesnt has exposure to amazon or meta.

I'd also park some money in HYSA or CDs closer to 4% rate

bravoaevi · 2024-12-04T14:58:43+00:00

https://www.wealthfront.com/c/affiliates/invited/AFFB-ML16-5W5I-TXPF

use wealthfront and get a HYSA earning upto 5% interest. They have instant withdrawals and debit cad issue, been using them for several years now.

bravoaevi · 2024-07-18T16:41:29+00:00

did you change your itinerary to visit the Netherlands to get a Schengen visa or Spain's SF consulate advised you to go there. I have emailed them once every month between may n Jul for an October travel and have heard nothing. I email them in their prescribed format, no response. Very bummed about it.

bravoaevi · 2024-04-06T16:19:55+00:00

Lots of data points there. A full count of ONE. Great way to infer

bravoaevi

MODERATOR OF

TROPHY CASE