Blocks session exit when the LLM hasn't proven its optimization with
real JMH benchmarks, hasn't tried enough techniques, or has strategies
remaining. Caps at 30 blocks to prevent infinite loops.
The agent was settling for easy wins and quitting targets after 1-2 failed
attempts. Now enforces:
- Minimum 10 attempts per target before skipping (15+ for high-impact, 20+ for ML)
- After a KEEP, try 3+ more approaches to find the maximum (not just first success)
- 10-category exploration ladder forces fundamentally different techniques each attempt
- Plateau requires 10+ consecutive discards across 5+ categories (was 3 discards)
- Exploration mindset section: first principles thinking, novel ideas, step-function improvements
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added mandatory checks for strategy plans in various experiment loops to ensure proper execution of assigned strategies.
- Updated target print statements to include strategy identifiers for better tracking of experiments.
- Emphasized the importance of workflow-level JMH comparisons in all experiments, not just after KEEPs, to ensure comprehensive performance evaluation.
- Clarified the necessity of comparing original and optimized code in every experiment to inform discard decisions accurately.
- Introduced guidelines for creating workflow-level benchmarks to capture full code paths and JIT behavior.
- Revised documentation to highlight the authoritative nature of workflow-level benchmarks over micro-benchmarks.
- Added a new JMH runner script (`jmh-runner.sh`) for automated benchmarking in Java, including options for baseline capture, GC profiling, and result comparison.
- Updated experiment loop documentation to include mandatory baseline performance capture before code changes, emphasizing the importance of capturing performance metrics for accurate comparisons.
- Revised micro-benchmarking steps to incorporate the new JMH runner and ensure consistent usage of GC profiling across benchmarks.
- Improved end-to-end benchmarking instructions to utilize the JMH runner for authoritative measurements, ensuring results are recorded alongside micro-benchmark results.
- Enhanced JSON result parsing in documentation to utilize `jq` for more efficient extraction of benchmark metrics.
- Streamlined the experiment loop base documentation to clarify the steps for capturing original outputs and performance baselines, reinforcing the need for correctness verification before proceeding with optimizations.
- Introduced a comprehensive guide on I/O & Serialization Optimization, covering data format choices, serialization libraries, buffer management, and common antipatterns.
- Added a detailed guide on Worker Pools and Process Management, focusing on CPU detection in containers, executor pool sizing, batch processing strategies, and lifecycle management.
- Introduced `e2e-benchmarks.md` for Java E2E benchmarking guidelines, including JMH detection, workflows, and fallback strategies.
- Created `micro-benchmark.md` detailing JMH micro-benchmarking practices, including benchmark design, execution, and result interpretation.
- Added `unified-profiling-script.sh` for comprehensive CPU, memory, and GC profiling using JFR, enhancing profiling capabilities for Java applications.
- Rewrite executive summary to reference his PR #1465 lockfile fix and
existing tooling (Renovate, Anchore, Chainguard)
- Reorder findings by category priority (supply chain > container > CI/CD)
to lead with what matters most to the audience
- Add animated parallelogram background matching codeflash.ai aesthetic
- 6 research-backed UX changes: severity icons (WCAG 1.4.1), title-first
cards (F-pattern), loss-framed 85% CTA, distinct status colors, card
opacity for figure-ground separation
- Correct SEC-021 from 67% to 97% mutable Action pins per VM verification
(only 2 of 96 SHA-pinned in core-product)
- Add talking-points-lawrence.md with profile, pain points, pitch strategy
Split the 39-finding wall into tabbed views matching the engagement
report pattern: Summary, Critical & High (21), Medium & Low (18),
and By Category with both category and repository breakdowns.
- Add build_jpc_view() with clean standalone layout at /jpc for JPC
(no tabs, no hero — just the document that "stands on its own")
- Add URL routing via dcc.Location: / serves full report, /jpc serves summary
- Add methodology notes to exec view (How This Was Tested annotations)
- Add methodology notes to detail view (7-entry "why" card)
- Enrich team view Memory + Standalone vs. Cumulative explanations
Team view:
- Add Engineering Impact Summary at top (4 metrics: memory, density,
latency, idle vCPU) with pointer to sections below
- Remove Production Context card (redundant with Impact Summary)
- Trim memory table to only metrics not shown in chart (RSS per
request, K8s allocation) — chart already shows pre/post/delta
- Fix "10-page scan" → "10-page scanned document" in methodology
Detail view:
- Add intro callout explaining this is the raw data backing the
other two views
Reorder based on persuasion research (Three-Talk Model, Prospect
Theory, Kotter):
1. "The Engagement" — collaborative shared context (team talk)
2. "What This Enables" — loss-framed enablement: 9.2x pod density,
41 idle vCPUs now available, -12.9% latency for agentic API
3. "The Results" — before/after proof of execution
4. Infrastructure Cost Impact (anchored on $100K/mo)
5. Workload Profiles + Methodology (credibility)
6. Delivered + Proposed Next Engagements
Key shift: lead with what the work unlocks (feature velocity,
platform capacity, API speed) rather than the technical achievement
(memory reduction). Cost savings is proof of execution, not the
headline.
The 1p/10p/16p benchmark rationale belongs in the exec view — JPC
needs to understand that page count != workload before seeing the
numbers. Added "Benchmark Workload Profiles" section before "How This
Was Tested" with the three profiles and the data punchline (#1505 at
-32.6% on 1 page vs -7.4% on 16 pages).
The 1p/10p/16p column headers weren't self-explanatory. Added a
"Benchmark Workload Profiles" card above the latency table in the
Detail view explaining that each document tests a distinct workload
shape (table-dense, scanned, mixed), not just different page counts.
Also added annotation below the table calling out that #1505 has 4x
the impact on the 1-page doc vs. the 16-page doc — letting the data
demonstrate that per-document cost depends on content, not page count.
- Reframe Future Engagements → Proposed Next Engagements based on
Crag meeting: lead with Platform API speed/stability, add
Infrastructure Cost Discovery ($100K/mo), remove Codeflash product
pitch
- Add Broader Context callout after cost section (core-product = ~10%
of total Azure spend)
- Fix Knative terminology throughout: "Knative pods" → "pods with a
1-CPU resource request" (CFS quota, not Knative config)
- Fix CPU detection description: three-tier logic (cgroup v2 cpu.max →
sched_getaffinity → os.cpu_count, take minimum)
- Clarify jemalloc is opt-in (MALLOC_IMPL=jemalloc), 1-CPU serial OCR
only; multi-CPU pods should use glibc default due to ~50 MB/process
arena overhead
Standalone security report (security_report.py) covering 6 supply chain
and build pipeline findings from the performance engagement. Add infra
cost section to exec view showing $10K → $1.1K/mo projection based on
D48s_v5 node packing at 4 GB vs 32 GB per pod.
* Update engagement report: add logos, grid theme, scope to core-product
- Add Codeflash x Unstructured logo lockup in hero and footer
- Apply roadmap grid pattern (48px, 5% opacity) and zinc-900 background
- Update cards to rounded-2xl with semi-transparent zinc-900/50 bg
- Remove all platform-libs, CI/CD, and security audit sections
- Remove stacked optimizations PR #1500 from open PRs
- Update data to latest FastAPI endpoint measurements
- Filter PR tables to core-product only
* Add methodology section to team view, fix DataTable type safety
Add benchmark environment, measurement protocol, and production
context cards to the top of the Engineering Team view. Split
TABLE_STYLE into individually typed constants (TABLE_HEADER,
TABLE_CELL, TABLE_DATA, TABLE_DATA_CONDITIONAL, TABLE_WRAP) so
DataTable kwargs pass ty and mypy strict checks.
* Add engagement report screenshot assets
* Add PRs from unstructured, unstructured-inference, unstructured-od-models
Expand report scope beyond core-product: 14 new merged PRs and 2 new
open PRs across 3 additional repos. Update PR counts (24 merged, 5 in
progress), add Repo column to detail view tables, update subtitle and
meta description.
* Make PR numbers clickable links in detail view tables
Use DataTable markdown columns with link_target=_blank so PR numbers
link to their GitHub PRs. Add REPO_BASES mapping for per-repo URL
resolution. Override default purple link color with blue (#60a5fa)
to stay readable on the dark background.
* main
* Add Future Engagements section with notes panels to exec view
Prominent banner heading, four numbered cards (CI/CD, Security, Runtime,
Product Integration) each with a right-hand Notes panel for discussion
points. Refactored _next_card helper to accept optional notes parameter.
* Exclude dev docs from plugin dist builds
README.md, ARCHITECTURE.md, and ROADMAP.md are development docs that
shouldn't ship in the assembled plugin distributions.
* Improve deep optimizer: fix profiling script, add failure mode awareness
Profiling script: Accept source root and command as CLI args instead of
hardcoding `src` and requiring manual `# === RUN TARGET HERE ===` edits.
The agent now copies the script from references and runs it with the
project's actual source root and test command.
Failure modes: Wire failure-modes.md into the on-demand reference table
and stuck recovery checklist so the agent consults it when workflows
break (deadlocks, silent failures, context loss, stale results).
* Fix ruff lint errors in unified profiling script
Refactor main() into parse_args(), profile_command(), and
report_results() to fix C901 (complexity) and PLR0915 (too many
statements). Also fix S306 (mktemp → NamedTemporaryFile), PLW1510
(explicit check=False), and add noqa for intentional os.path usage
(PTH112) and subprocess with CLI args (S603).
Remove .codeflash/ from ruff extend-exclude, add per-file ignores
for .codeflash/, scripts/, evals/, and plugin/ (benchmark/script
patterns like print, eval, magic values). Remove shebangs. Widen
pre-commit hooks to check the full repo.
* Add Unstructured engagement report as uv workspace member
Three-tier Plotly Dash app (Executive Brief, Engineering Team, Full
Detail) with data in JSON, theme constants in theme.py, and Dash
production improvements (Google Fonts, clientside callbacks, meta tags).
Also: add .playwright-mcp/ to .gitignore, add reports/* ruff overrides,
remove tracked .codeflash/observability/read-tracker.
* Rewrite statusline to derive context from git state
Detects active area from changed files (reports, packages, plugin,
.codeflash, case-studies, evals), falls back to branch name convention
(perf/*, feat/*, fix/*), shows dirty indicator. Uses whoami for
cross-platform user detection.
* Add pre-push lint rule to commit guidelines
* Exclude .codeflash/ from ruff linting
Benchmark and profiling scripts in .codeflash/ are scratch work, not
package source. Excluding them prevents CI failures from ad-hoc scripts.
* Run ruff format across packages, scripts, evals, and plugin refs
* Fix github-app async test failures in CI
Add asyncio_mode = "auto" to root pytest config so async tests
are detected when running from the repo root via uv run pytest packages/.
* Fix mypy errors and apply ruff formatting across packages
Fix ast.FunctionDef calls missing type_params for Python 3.12+,
correct type: ignore error codes in _comparator and _plugin, and
run ruff format on all package source and test files.
* Switch CI to prek for lint/typecheck checks
Use j178/prek-action for consistent lint+typecheck (ruff check,
ruff format, interrogate, mypy) matching local pre-commit config.
Keep test as a separate parallel job for test-env support.
Replace packages-ci.yml and github-app-tests.yml with a single
ci.yml that calls the shared ci-python-uv reusable workflow.
Lint, typecheck, and test run as parallel jobs. Version check
stays local (needs fetch-depth: 0 + PR-only conditional).
Replace placeholder text ("No optimizations applied yet", empty PR table) with:
- CAS lz4 compression results (7-18x on realistic ML payloads)
- Upstream PR status (Netflix/metaflow#3090, open)
- Open questions on dependency management and forward compat
- Methodology, remaining targets, and lessons learned
- Rename case-studies/pypa/ → case-studies/python/ to match .codeflash/ convention
- Add case-studies/netflix/metaflow/summary.md (7-18x lz4 vs gzip)
- Add case-studies/unstructured/core-product/summary.md (14.6% latency, 2.1 GB memory)
- Update main README results table with all five case studies
Move vector search benchmarks out of main results into a Lessons Learned
section. The 3.7x-14.2x numbers were real but on a non-bottleneck —
maintainer confirmed model API calls and SQL dominate real latency.
Results section now only shows legitimate wins: import time (1.16x),
indexing pipeline (1.14-1.16x), and query batching (2.10-2.62x).
Add team member dimension to case study paths so multiple contributors
can track optimization data independently. Derives member from
git config user.name in session-start hooks.
- Move all case studies under .codeflash/krrt7/
- Rename pypa/pip → python/pip (org grouping)
- Update session-start hooks, docs, scripts, and references
