Weave "optimizations reveal deeper issues" framing into engagement report
executive summary, case study, and optimization README. Add O(N²) text
extraction fix, per-request RSS creep (24→17 MB), and memray profiling
data that were previously undocumented.
Apply research-backed case study structure: headline anchoring on
biggest numbers, customer-as-hero framing, loss aversion, narrative
arc, methodology for developer credibility. Collapse PR inventory
to category summary, ~1,100 words in optimal range.
- 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
* Move Go plugin overlay from languages/go/ to plugin/languages/go/
Aligns Go with the Java/Python/JavaScript convention where all language
overlays live under plugin/languages/<lang>/. The Makefile already
discovers from plugin/languages/* so Go is now included in builds.
* Remove accidental read-tracker changes
* Ignore .codeflash/observability/ in gitignore
* Add Java/Kotlin detection to top-level language router
Adds pom.xml, build.gradle, build.gradle.kts, settings.gradle, and
settings.gradle.kts as markers that route to the codeflash-java router.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add Java/Kotlin agent definitions for all optimization domains
10 agents covering the full optimization pipeline:
- codeflash-java: router/team lead for domain detection
- codeflash-java-setup: environment detection (build tool, JDK, profiling tools)
- codeflash-java-deep: cross-domain optimizer (default)
- codeflash-java-cpu: data structures, algorithms, JIT deopt, JMH benchmarks
- codeflash-java-memory: heap/GC tuning, escape analysis, leak detection
- codeflash-java-async: virtual threads, lock contention, CompletableFuture
- codeflash-java-structure: class loading, JPMS, startup time, circular deps
- codeflash-java-scan: quick cross-domain diagnosis via JFR/jdeps/GC logs
- codeflash-java-ci: GitHub webhook handler for Java PRs
- codeflash-java-pr-prep: JMH benchmarks and PR body templates
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add Java domain reference guides for all optimization domains
6 guides covering deep domain knowledge for agent consumption:
- data-structures: collection selection, autoboxing, JIT patterns, sorting
- memory: JVM heap layout, GC algorithms and tuning, escape analysis, leaks
- async: virtual threads, structured concurrency, lock hierarchy, contention
- structure: class loading, JPMS, CDS/AppCDS, ServiceLoader, Spring startup
- database: JPA N+1, HikariCP, pagination, batch operations, EXPLAIN plans
- native: JNI, Panama FFM API, GraalVM native-image, Vector API
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add Java optimization skills: session launcher and JFR profiling
- codeflash-optimize: session launcher with start/resume/status/scan/review
- jfr-profiling: quick-action JFR profiling in cpu/alloc/wall modes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Slim Java agents to match Go's concise ~175-line pattern
Move inline code examples, antipattern encyclopedias, JMH templates,
and deep-dive sections from agent prompts into reference guides.
Agents now contain only: target tables, one-liner antipatterns,
reasoning checklists, profiling commands, and keep/discard trees.
Line counts (before → after):
cpu: 636 → 181
memory: 878 → 193
async: 578 → 165
structure: 532 → 167
deep: 507 → 186
scan: 440 → 163
Average: 595 → 176 (vs Go's 175)
Adds to data-structures/guide.md:
- Collection contract traps table
- Reflection → MethodHandle migration pattern
- JMH benchmark template
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Fix Makefile build: use rsync merge and portable sed -i
Two bugs in the build target:
1. cp -R created nested dirs (agents/agents/, references/references/)
instead of merging language overlay into shared base. Fix: rsync -a.
2. sed -i '' is macOS-only; fails silently on Linux. Fix: sed -i.bak
(works on both macOS and Linux), then delete .bak files.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add HANDOFF.md session lifecycle to Java agents
Java agents could read HANDOFF.md on resume but never wrote or
updated it. A session that hit plateau would lose all context —
what was tried, what worked, why it stopped, what to do next.
Changes:
- Deep agent: init HANDOFF.md on fresh start, record after each
experiment, write Stop Reason + learnings.md on session end
- Domain agents (CPU, memory, async, structure): record to
HANDOFF.md after each keep/discard, write session-end state
- Handoff template: make language-agnostic (was Python-specific),
add Session status, Strategy & Decisions, and Stop Reason fields
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Close 11 gaps between Java and Python plugins
Add missing sections to Java deep agent: experiment loop depth (12 steps),
library boundary breaking, Phase 0 environment setup, CI mode, pre-submit
review, adversarial review, team orchestration, cross-domain results schema,
and structured progress reporting.
Add polymorphic dispatch safety to CPU agent and data-structures guide.
Add diff hygiene to CPU agent. Add native reference to router.
Create two new reference files: library-replacement.md (Guava/Commons/
Jackson/Joda replacement tables) and team-orchestration.md (full dispatch
and merge protocol).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
- Setup agent now ensures .codeflash/ is in .gitignore before writing
session state files (prevents accidental commits of profiling artifacts)
- Router agent gets a Cleanup section: preserves learnings.md and
results.tsv across sessions, deletes transient files (HANDOFF.md,
setup.md, conventions.md, bench scripts), removes agent-memory dir
