100 messages across 3 sessions | 2026-09-01 to 2026-09-01
At a Glance
What's working: You run Claude like a research collaborator, not a code generator: plan first, gate execution, then demand measured evidence before accepting a theory. That discipline paid off in the AVX-512 work, where profiling refuted most of the initial hypotheses and pointed at the real port-5 shuffle and dependency-chain bottlenecks, yielding a verified ~16-21% prompt-processing gain. You also handled negative results well, reverting regressions cleanly and keeping verified wins on their own branch so the tree stayed trustworthy through long experimental stretches. Impressive Things You Did →
What's hindering you: On Claude's side: it anchored on plausible-but-wrong explanations early (a misidentified constant, a flawed port assumption) and pushed optimizations that regressed before profiling had actually located the bottleneck. On your side: toolchain gaps like missing perf and clang surfaced mid-experiment rather than up front, and a very ad-hoc shell workflow meant a quoting mistake left an orphaned background job running unnoticed for hours while a benchmark crash stalled a whole sweep. Where Things Go Wrong →
Quick wins to try: Try a Custom Skill for your standard optimization loop — verify toolchain, capture baseline, profile, hypothesize, build, benchmark, keep-or-revert — so every experiment follows the same rigor without you re-specifying it. Add Hooks to auto-clean background processes and log benchmark output at session end, which would have caught the orphaned wait task. And lean on Task Agents to explore code paths or scan for candidate hot spots in parallel while you stay focused on the measurement loop. Features to Try →
Ambitious workflows: Start structuring your work so hypotheses can be raced rather than queued: separate git worktrees, a scripted build-bench-report cycle, and a machine-readable results format. With stronger models, you can dispatch several subagents to each implement and benchmark a distinct micro-optimization simultaneously and hand you a leaderboard of measured deltas instead of a serial trail of reverts. The same scaffolding unlocks unattended overnight campaigns — full quantization/context sweeps that catch crashes as reproducible bug reports, and an auto-revert harness where only statistically significant wins survive until morning. On the Horizon →
The core focus across all sessions was accelerating prompt processing in ik_llama.cpp on Cascade Lake hardware using AVX-512 and VNNI instructions. Claude diagnosed a port-5 shuffle bottleneck and dependency-chain latency, then implemented broadcast and instruction-level-parallelism accumulator changes that delivered a measured ~16-21% throughput improvement. Work was planned first via ExitPlanMode, then executed with heavy Edit and Bash usage against the C++ kernel code.
Performance Profiling and Hypothesis Testing~2 sessions
Claude used perf to profile hot loops and systematically test optimization hypotheses rather than guessing. Several hypotheses were refuted by measurement, including a width-4 kernel restriction and a paired accumulator helper that both regressed performance and were reverted. An early misidentification of the k_x_step constant and a flawed port-1 assumption were caught and corrected through further profiling.
Quantization and KV-Cache Benchmarking~3 sessions
Beyond kernel work, Claude ran comparative quantization sweeps and long-context KV-cache benchmarks, including perplexity analysis across formats. Most runs completed successfully, though the q8_KV configuration crashed on a GGML assertion. Bash dominated tool usage here for launching and monitoring long-running benchmark jobs.
Codebase Exploration and Architecture Mapping~1 sessions
Claude performed a read-only survey of the MTP and KV-cache code paths to build a map of how inference data flows through the project. This grounded later optimization decisions and produced Markdown documentation, which was the largest language by volume in the dataset. Read and Agent tools supported this exploration without modifying source.
Build Environment and Version Control~2 sessions
Missing tooling such as perf and clang had to be installed mid-session before profiling and compilation could proceed, and enabling clang unlocked embedded broadcast in vpdpbusd for a real win. Claude committed 15 changes across a dedicated optimization branch, keeping verified wins separate from reverted experiments. A shell-quoting bug also left an orphaned background wait task running for hours before it was noticed.
What You Wanted
Performance Optimization
6
Benchmarking Experiments
3
Testing And Benchmarking
2
Codebase Exploration Planning
1
Code Explanation
1
Version Control Commit
1
Top Tools Used
Bash
681
Edit
103
Read
91
Write
7
ExitPlanMode
6
Agent
3
Languages
Markdown
109
C++
80
C
12
Session Types
Iterative Refinement
2
Exploration
1
How You Use Claude Code
You operate in long-horizon research mode: three sessions spanning 54 hours with only ~100 messages means you hand Claude a substantial problem — AVX-512/VNNI prompt-processing throughput on Cascade Lake — and then let it run for extended stretches without interruption. The tool distribution tells the story: 681 Bash calls against just 103 Edits and 7 Writes. You're not asking for code to be written; you're asking for experiments to be conducted. Build, profile with perf, benchmark, revert, rebuild. The edit-to-execute ratio of roughly 1:7 is the signature of someone treating Claude as a lab assistant rather than a code generator.
Your workflow consistently begins with planning — ExitPlanMode appears six times and every session summary opens with "user wanted a plan and then execution." You want the hypothesis space mapped before anyone touches a kernel. Crucially, you tolerate negative results as first-class outcomes: the width-4 kernel restriction and paired-accumulator helper both regressed and were reverted, three optimization hypotheses were refuted outright, and none of this registered as failure. Satisfaction stayed high (2 happy, 5 satisfied, 17 likely satisfied) even through a q8_KV GGML assertion crash and a shell-quoting bug that left an orphaned background wait running for hours. You measure success by whether a claim was *verified*, not whether it was *confirmed* — the ~16-21% pp win from embedded broadcast plus ILP accumulators landed precisely because the failed branches were honestly killed.
The environment friction — perf and clang missing, installed mid-session — you absorbed without derailing. Similarly, when Claude's initial handover recommendation rested on a flawed port-1 assumption, the correction happened in-flight rather than triggering a restart. This suggests you read Claude's reasoning closely enough to catch bad premises but intervene surgically rather than taking the wheel. The 15 commits across branch work, the 109 Markdown files alongside 80 C++ ones, and the read-only mapping of MTP/KV-cache paths all point to the same habit: you want the investigation documented as thoroughly as it's executed, because the writeup is part of the deliverable.
Key pattern: You commission plan-first performance investigations and then let Claude run for hours largely uninterrupted, treating refuted hypotheses and reverted experiments as valuable output rather than wasted effort.
User Response Time Distribution
2-10s
0
10-30s
10
30s-1m
6
1-2m
22
2-5m
33
5-15m
9
>15m
8
Median: 130.6s • Average: 335.8s
Multi-Clauding (Parallel Sessions)
3
Overlap Events
3
Sessions Involved
40%
Of Messages
You run multiple Claude Code sessions simultaneously. Multi-clauding is detected when sessions
overlap in time, suggesting parallel workflows.
User Messages by Time of Day
Morning (6-12)
14
Afternoon (12-18)
15
Evening (18-24)
21
Night (0-6)
50
Tool Errors Encountered
Command Failed
10
Other
5
User Rejected
3
Impressive Things You Did
Across three long sessions of low-level AVX-512/VNNI optimization work on ik_llama.cpp, you drove a rigorous, measurement-first performance campaign that landed a verified 16-21% prompt-processing speedup.
Plan first, then execute
You consistently asked for an experimental plan before letting Claude touch code, using ExitPlanMode as a real gate rather than a formality. That discipline meant each optimization attempt had a stated hypothesis and a success criterion, so even the failed experiments produced usable information instead of wasted commits.
Profiling over guesswork
Rather than accepting plausible-sounding theories, you pushed the work toward actual perf profiling, which refuted most of the initial hypotheses and surfaced the real culprits: a port-5 shuffle bottleneck and dependency-chain latency. The winning changes, embedded broadcast in vpdpbusd and an ILP accumulator restructure, came directly from that hardware-level evidence rather than from intuition.
Reverting negative results cleanly
When the width-4 kernel restriction and paired accumulator helper both regressed throughput, you treated them as data and reverted rather than trying to rescue sunk work. Combined with committing wins incrementally on a branch across 15 commits, this kept the tree in a known-good state through 54 hours of aggressive experimentation.
What Helped Most (Claude's Capabilities)
Good Debugging
3
Outcomes
Mostly Achieved
2
Fully Achieved
1
Where Things Go Wrong
Across your three long AVX-512/VNNI optimization sessions, friction came mostly from hypothesis-driven micro-optimizations that regressed and had to be reverted, missing profiling tooling discovered mid-session, and shell/background-process management mishaps during heavy Bash usage.
Speculative optimizations that regressed and needed reverting
You repeatedly landed on optimization hypotheses before profiling confirmed the bottleneck, which cost cycles on changes that made throughput worse. Front-loading a perf run and requiring a measured hypothesis before any kernel edit would cut the revert churn.
The width-4 kernel restriction and paired accumulator helper both regressed performance and had to be reverted, burning benchmark cycles for zero gain
An initial handover recommendation (Option B) rested on a flawed port-1 assumption that later had to be corrected, propagating a wrong mental model into the next session
Environment and tooling gaps discovered mid-flight
Core profiling and compiler tooling wasn't present when you needed it, so sessions stalled to install dependencies instead of measuring. Verifying that perf, clang, and benchmark harnesses run cleanly before starting the optimization loop would keep the investigation continuous.
perf and clang had to be installed partway through a session, delaying the profiling that ultimately located the port-5 shuffle bottleneck
The q8_KV long-context KV-cache benchmark crashed on a GGML assertion, leaving that arm of the quantization sweep incomplete
Bash-heavy workflow and background process hygiene
With 681 Bash calls dominating your tool usage, small shell mistakes had outsized consequences on long-running benchmark jobs. Wrapping long benchmarks in a script with explicit logging and cleanup, rather than ad-hoc inline commands, would make failures visible and recoverable.
A shell-quoting bug left an orphaned background wait task running for hours, consuming machine resources during timing-sensitive benchmarks
Pointing at the wrong k_x_step constant sent two experiments down an incorrect path before the actual constant was identified
Primary Friction Types
Wrong Approach
4
Failed Optimization Attempt
3
Buggy Code
2
Environment Tooling Gap
2
Inferred Satisfaction (model-estimated)
Likely Satisfied
17
Satisfied
5
Happy
2
Existing CC Features to Try
Suggested CLAUDE.md Additions
Just copy this into Claude Code to add it to your CLAUDE.md.
All three sessions were AVX512/VNNI prompt-processing optimization work where several hypotheses (width-4 kernel restriction, paired accumulator helper) regressed and had to be reverted after the fact.
A session was interrupted because perf and clang were missing and had to be installed partway through profiling.
Multiple sessions produced negative results (width-4 kernel, paired accumulators) that required reverts and a corrected handover recommendation, so a written trail avoids re-testing dead hypotheses.
A shell-quoting bug left an orphaned background wait task running for hours in one session.
A handover recommendation (Option B) was built on a flawed port-1 assumption and had to be corrected later.
Just copy this into Claude Code and it'll set it up for you.
Custom Skills
Reusable markdown prompts you invoke with a single slash command.
Why for you: You ran the same measure-baseline / patch / re-measure / revert-if-regressed loop across all three sessions with 681 Bash calls — a /bench skill would collapse that into one command with consistent methodology.
mkdir -p .claude/skills/bench && cat > .claude/skills/bench/SKILL.md <<'EOF'
---
name: bench
description: Run the AVX512 pp benchmark and compare against the recorded baseline
---
1. Read EXPERIMENTS.md for the current baseline numbers and command.
2. Build with the pinned toolchain: `cmake --build build -j$(nproc)`.
3. Run the exact baseline bench command 3x, redirect to /tmp/bench-$(date +%s).log, print the PID.
4. Report median pp t/s and the percent delta vs baseline.
5. If the delta is negative, immediately `git stash` the change and append a negative-result entry to EXPERIMENTS.md (hypothesis, diff summary, measured delta, likely cause).
EOF
Hooks
Shell commands that auto-run at lifecycle events like tool use or session end.
Why for you: You lost hours to an orphaned background wait task from a shell-quoting bug; a Stop hook that reaps stray bench processes would have caught it automatically.
Claude spawns focused subagents for exploration or parallel investigation.
Why for you: You only used Agent 3 times despite doing read-only mapping of MTP/KV-cache code paths and testing multiple independent optimization hypotheses — subagents can explore each hypothesis in parallel without polluting your main context with 681 Bash calls.
claude "Use three parallel agents: one to map every AVX512 quantized dot-product kernel in ggml-quants.c and report which use vpdpbusd, one to trace the KV-cache q8_KV path and find the GGML assertion that crashes, and one to read EXPERIMENTS.md and summarize which hypotheses are already refuted. Do not edit any files."
New Ways to Use Claude Code
Just copy this into Claude Code and it'll walk you through it.
Keep a running experiment ledger
Maintain an EXPERIMENTS.md with hypothesis, change, measured delta, and verdict so refuted ideas never get retried.
Across your three sessions you refuted most initial hypotheses and had at least two changes (width-4 kernel restriction, paired accumulator helper) regress and require reverts. Because the sessions were long (54 hours) and handed off, a flawed port-1 assumption survived into a recommendation. A durable ledger makes negative results as valuable as positive ones and makes handovers safe.
Paste into Claude Code:
Create EXPERIMENTS.md at the repo root. Populate it from git log and our prior work with one row per optimization hypothesis: date, hypothesis, files touched, measured pp t/s before and after, verdict (WIN/REGRESSION/NEUTRAL), and the microarchitectural reason. Mark the width-4 kernel restriction and paired accumulator helper as REGRESSION with the reason. Then, from now on, append to this file before you try any new hypothesis.
Front-load environment checks into a plan gate
Make 'verify toolchain' the first step of every plan so perf/clang installs don't interrupt an experiment mid-flight.
You used ExitPlanMode 6 times, so you're already planning well, but a session stalled because perf and clang weren't present. Adding a hard prerequisite-check step to the plan template means the interruption happens at minute one instead of during a long benchmark. This also lets you pin the compiler explicitly, which mattered since clang was what enabled embedded broadcast in vpdpbusd.
Paste into Claude Code:
Before proposing any performance plan, first run a prerequisite check: verify perf, clang, cmake, and the bench binaries exist and print their versions; confirm /proc/cpuinfo shows avx512_vnni; confirm perf_event_paranoid allows profiling. Report anything missing with the exact install command, and only then propose the experiment plan.
Isolate risky benchmarks so one crash doesn't kill the sweep
Run KV-quantization sweeps as a driver script that catches per-config failures instead of one long chained command.
Your q8_KV KV-cache benchmark crashed on a GGML assertion during a long-context sweep, which risks losing results for configs that would have succeeded. A driver loop that logs each config independently, captures the assertion text, and continues gives you a complete comparison table plus a clean reproducer for the crash. This pairs well with your 681 Bash calls — one script replaces dozens of interactive invocations.
Paste into Claude Code:
Write scripts/kv_sweep.sh that loops over KV cache type combinations (f16, q8_0, q8_KV, q4_0) x context lengths, runs each in a subshell with a timeout, redirects stdout/stderr to logs/kv_<type>_<ctx>.log, records exit code, and continues on failure. At the end emit a markdown table of pp t/s, tg t/s, and status. Then run it and give me a minimal standalone reproducer for any config that hit a GGML assertion.
Automate the post-change verification loop
Use headless mode to run build + bench + perplexity checks in the background between interactive experiments.
With 15 commits and heavy Bash usage over 54 hours, a lot of your time went to mechanical rebuild-and-measure cycles. Driving those from a script with `claude -p` lets you queue verification for the last change while you reason about the next hypothesis. It also produces a consistent, reviewable log rather than ad-hoc terminal scrollback.
Paste into Claude Code:
Set up a verification script that I can call as: claude -p "Rebuild, run the standard pp benchmark 3x, run perplexity on the reference file, and append a PASS/FAIL verdict plus numbers to EXPERIMENTS.md. Fail loudly if pp t/s regressed more than 1% vs the recorded baseline." --allowedTools "Bash,Read,Edit" — and tell me exactly what to put in the script.
On the Horizon
Your sessions show AI-assisted development maturing from single-threaded pair programming into empirical performance engineering, where Claude forms hypotheses, measures them with perf, and reverts the losers on its own.
Parallel Hypothesis Racing With Subagents
Instead of testing AVX512 optimization hypotheses serially — where three of your ideas regressed and had to be reverted — dispatch a fleet of subagents, each on its own git worktree, to implement and benchmark a distinct micro-optimization simultaneously. Each agent builds, runs the pp benchmark, records cycles/instructions/port-pressure from perf, and reports back a verdict with numbers. You review a leaderboard of measured results instead of a sequence of dead ends, turning negative results from wasted hours into cheap parallel data points.
Getting started: Use the Agent/Task tool to spawn one subagent per hypothesis, each operating in a separate `git worktree add` directory so builds don't collide, and have them all write results to a shared markdown scoreboard.
Paste into Claude Code:
I want to test 5 AVX512/VNNI prompt-processing optimization hypotheses in parallel rather than serially. First, read the relevant kernel code and propose 5 distinct, independent hypotheses (e.g. embedded broadcast, accumulator ILP depth, shuffle elimination on port 5, unroll factor, prefetch distance). For each one: create a separate git worktree under ../opt-experiments/<name>, spawn a subagent that implements ONLY that change, builds with the same flags, runs the pp512 benchmark 3 times, and captures `perf stat -e cycles,instructions,uops_dispatched_port.port_5` output. Each agent must write a row to BENCH_SCOREBOARD.md with: hypothesis, delta vs baseline (%), stddev, port-5 uop delta, and a verdict of WIN/NEUTRAL/REGRESSION. Establish and record the baseline number FIRST before launching any agents. When all agents finish, show me the scoreboard sorted by speedup, and only then propose which winners to combine and cherry-pick into a single branch.
Self-Verifying Benchmark Harness With Auto-Revert
Build a harness that makes performance a test suite: any change that doesn't beat the baseline by a statistically significant margin is automatically reverted before you ever see it. Claude can then run an overnight optimization loop — mutate, build, benchmark, keep-or-revert — accumulating only verified wins, with a full audit log of every rejected hypothesis. Your ~16-21% speedup becomes the floor of an autonomous ratchet rather than the ceiling of a manual session.
Getting started: Write a `bench.sh` that emits JSON with confidence intervals, wire it into a loop with `git stash`/`git revert` on failure, and combine with ScheduleWakeup so long benchmark sweeps continue unattended.
Paste into Claude Code:
Build me an autonomous performance ratchet for this repo. Step 1: create scripts/bench.sh that runs the prompt-processing benchmark N=5 times, discards the first as warmup, and prints JSON {mean_tps, stddev, ci95}. Step 2: create scripts/ratchet.sh that (a) records the current committed baseline into .bench-baseline.json, (b) builds and benchmarks the working tree, (c) if the improvement is not at least 2% AND outside the 95% CI of the baseline, prints REJECTED and runs `git checkout -- .` to auto-revert, (d) if it wins, commits with the measured delta in the commit message and updates the baseline. Step 3: make it robust to build failures and to benchmark crashes (like the q8_KV GGML assertion I hit) — treat a crash as REJECTED, never as a hang. Step 4: verify the whole harness works by deliberately introducing a known-bad change and confirming it auto-reverts. Then use this harness for every future optimization attempt instead of eyeballing numbers.
Autonomous Overnight Quantization Sweep Agent
Turn your KV-cache and quantization comparisons into an unattended overnight campaign: an agent that enumerates the full matrix of quant types by context length, runs perplexity and throughput for each cell, catches crashes like the q8_KV assertion, files them as reproducible bug reports instead of stalling, and wakes up in the morning with a complete results table plus plots. Long-running sweeps that ate hours of your 54-hour session run while you sleep. The agent also guards against the orphaned-background-wait bug by tracking every PID it spawns and reaping them on exit.
Getting started: Combine ScheduleWakeup for periodic check-ins with a resumable job queue file so the sweep survives interruption, and have the agent emit a markdown report with a results matrix and per-failure repro commands.
Paste into Claude Code:
Design and run an unattended quantization sweep. Step 1: write sweep_queue.jsonl with one line per experiment cell covering {KV cache types: f16, q8_0, q8_KV, q4_0} x {context: 4k, 16k, 64k} x {metrics: pp throughput, tg throughput, perplexity}. Step 2: write a runner that pops the next PENDING cell, runs it with a hard timeout, and marks it DONE/FAILED/TIMEOUT in place so the sweep is fully resumable if interrupted. Step 3: CRITICAL — every background process must be launched with a recorded PID and killed in a trap/EXIT handler; I previously left an orphaned wait task running for hours due to a shell-quoting bug, so validate your quoting by dry-running the command construction first. Step 4: on any crash (e.g. a GGML assertion), capture the full stderr, the exact repro command, and a stack trace into failures/ as a filed bug — then CONTINUE the sweep rather than halting. Step 5: use ScheduleWakeup to check progress periodically. Step 6: when complete, produce SWEEP_REPORT.md with a markdown results matrix, a quality-vs-speed Pareto analysis, and a ranked recommendation of which quantization to use at which context length.
"A shell-quoting typo spawned an orphaned background 'wait' task that quietly ran for hours while Claude chased a port-5 shuffle bottleneck — the AVX-512 optimization session accidentally optimized a process into immortality"
During the 54-hour AVX512/VNNI prompt-processing push on ik_llama.cpp (681 Bash calls deep), Claude refuted most of its own hypotheses — two experiments regressed and had to be reverted, and a handover recommendation was built on a wrong port-1 assumption — before landing the real win: clang-enabled embedded broadcast in vpdpbusd plus an ILP accumulator change, for a verified 16-21% speedup.