DailyDiff ⚡

We scan the noise. Five things survive.
An autonomous multi-agent tech intelligence pipeline built with LangGraph, FastAPI, and React. Designed for computer science students and early-career software engineers: it turns developer chatter into up to five fact-checked briefs every Monday, Wednesday, and Friday.

🌐 Live Deployment: https://dailydiff.in

🌟 Why DailyDiff?

Developer feeds are saturated with corporate PR announcements, repetitive framework patch notes, and speculative academic research. DailyDiff automates the editorial triage:

Scrapes raw ecosystem signals from GitHub search, GitHub releases (React, Next.js, FastAPI, Tailwind CSS, Go), Hacker News, Dev.to, freeCodeCamp, Product Hunt, and Reddit /r/webdev.
Filters out enterprise fluff & repeats by deduplicating against every previously published URL, then asking an LLM to keep only items that are new, substantial, and useful.
Scrapes source documentation (GitHub READMEs or page text, capped at 1,800 characters) to ground every claim.
Fact-checks, analyzes, and writes in one batched LLM call per stage, using a Groq → Mistral fallback chain.
Enforces a fail-closed publish gate: if an agent fails or too few briefs pass the rule checks, nothing is published and no email is sent.
Empirically evaluated with live LLM calls, Wilson confidence intervals, planted hallucination traps, and prompt-injection cases.

🏗️ System Architecture

flowchart TD
    %% Subgraph 1: Signal Ingestion
    subgraph Sources["🌐 1. Raw Developer Ecosystem Sources"]
        direction TB
        S1["🐙 GitHub Search & Releases"]
        S2["📰 Hacker News (Top 15)"]
        S3["💻 Dev.to & freeCodeCamp RSS"]
        S4["🚀 Product Hunt & Reddit /r/webdev"]
    end

    %% Subgraph 2: Multi-Agent Intelligence Core
    subgraph Pipeline["🧠 2. LangGraph Multi-Agent Pipeline (linear)"]
        direction TB
        N1["🔍 Node 1: Scout<br/>Sequential HTTP fetches (~60 raw signals)"]
        N2["🛡️ Node 2: Skeptic (1 LLM call)<br/>URL dedup vs history + relevance filter (≤ 10 candidates)"]
        N3["📚 Node 3: Researcher<br/>README / page scraper (≤ 1,800 chars)"]

        subgraph LLMLayer["⚡ Provider Fallback Chain (30s timeout each)"]
            LLM1["Groq openai/gpt-oss-120b"]
            LLM2["Groq qwen/qwen3.8-27b"]
            LLM3["Groq openai/gpt-oss-20b"]
            LLM4["Mistral mistral-small-latest"]
            LLM1 -.->|on error| LLM2 -.->|on error| LLM3 -.->|on error| LLM4
        end

        N4["🔬 Node 4: Verifier (1 batched call)<br/>Checks claims against scraped evidence"]
        N5["📊 Node 5: Analyst (1 batched call)<br/>why_it_matters, who_cares, verdict (WATCH / INTEGRATE / READ)"]
        N6["✍️ Node 6: Editor (1 batched call)<br/>Up to 5 briefs, one per category, bold lead sentence"]

        N1 --> N2
        N2 --> N3
        N3 --> N4
        N4 --> N5
        N5 --> N6

        LLMLayer -.->|Inference| N2
        LLMLayer -.->|Inference| N4
        LLMLayer -.->|Inference| N5
        LLMLayer -.->|Inference| N6
    end

    %% Subgraph 3: Fail-Closed Publish Gate
    subgraph GateSection["🛡️ 3. Fail-Closed Publish Gate (run_agent.py)"]
        direction TB
        QG{"Publish Gate"}
        Inv["Per-brief rule checks (checks.py):<br/>• Required fields, valid category & verdict<br/>• Bold lead sentence, no TL;DR label<br/>• HTTPS source URL that was a candidate<br/>• No generic filler text<br/>• Every number found in the evidence<br/>Run level: no node failures, ≥ 3 briefs survive"]
        QG --- Inv
    end

    %% Subgraph 4: Publication & Delivery
    subgraph Delivery["🚀 4. Publication & Delivery"]
        direction TB
        D_Git["🗂️ data/history.json + data/archive/ (committed by CI)"]
        D_UI["💻 React 19 + Vite site (dailydiff.in)<br/>Static history cache + live API merge"]
        D_Email["📬 FastAPI /api/notify-subscribers<br/>Brevo → Gmail SMTP → Resend"]
    end

    %% Subgraph 5: Safety
    subgraph Safety["🛑 Blocked Run"]
        FailNode["⚠️ Publication Blocked<br/>data/runs/YYYY-MM-DD.degraded.json<br/>Exit code 1 • No emails"]
    end

    %% Connections
    Sources --> N1
    N6 --> QG
    QG ==>|✅ PASS| D_Git
    QG ==>|✅ PASS| D_Email
    D_Git --> D_UI
    QG -->|❌ FAIL| FailNode

    %% Styling
    classDef sourceStyle fill:#1e293b,stroke:#475569,stroke-width:1px,color:#f8fafc;
    classDef agentStyle fill:#0f172a,stroke:#3b82f6,stroke-width:2px,color:#f8fafc;
    classDef llmStyle fill:#1e1b4b,stroke:#818cf8,stroke-width:1px,color:#e0e7ff;
    classDef gateStyle fill:#1e293b,stroke:#f59e0b,stroke-width:2px,color:#fef3c7;
    classDef passStyle fill:#064e3b,stroke:#10b981,stroke-width:2px,color:#ecfdf5;
    classDef failStyle fill:#450a0a,stroke:#ef4444,stroke-width:2px,color:#fef2f2;

    class S1,S2,S3,S4 sourceStyle;
    class N1,N2,N3,N4,N5,N6 agentStyle;
    class LLM1,LLM2,LLM3,LLM4 llmStyle;
    class QG,Inv gateStyle;
    class D_Git,D_UI,D_Email passStyle;
    class FailNode failStyle;

Pipeline Nodes

Stage	Node	File	Responsibility
1	Scout	`backend/app/agents/scout.py`	5 GitHub repository searches (recently pushed, mostly created in the last 45 days), latest releases of 5 tracked repos, HN top 15, and RSS/Atom feeds. Look-back window is 2 days (3 on Mondays).
2	Skeptic	`backend/app/agents/skeptic.py`	Drops any URL already in `data/history.json`, keeps the first 45 signals (descriptions cut to 200 chars), shows the model the last 7 editions' titles, and asks for the IDs to keep. Caps output at 10 candidates.
3	Researcher	`backend/app/agents/researcher.py`	GitHub: raw `README.md` from `main`/`master`, falling back to repo metadata. Other links: visible page text via BeautifulSoup. Capped at `MAX_EVIDENCE_CHARS = 1800`. Records the content source per URL.
4	Verifier	`backend/app/agents/verifier.py`	Drops candidates with no usable evidence (`empty`, `description_only`, `api_fallback`; releases are exempt), then 1 batched call marks each VALID / INVALID. Only VALID items continue.
5	Analyst	`backend/app/agents/analyst.py`	1 batched call adds `why_it_matters`, `who_cares`, and a verdict (`WATCH`, `INTEGRATE`, `READ`). Items with missing or invalid output are dropped with a warning.
6	Editor	`backend/app/agents/editor.py`	1 batched call selects up to 5 items, one per category (`Tech Shift`, `Cool Tool`, `Career & Learning`, `Ecosystem Update`, `Watchlist`), and writes a bold lead sentence.
Gate	Publish Gate	`backend/run_agent.py`	Runs the rule checks from `backend/app/checks.py` on each brief, drops failures and duplicate categories, then publishes or blocks.

🛡️ Reliability & Fault-Tolerance Principles

Fail-closed nodes: every LLM node catches its own failure, records it in run_health.py, and returns an empty list. Any recorded failure blocks publication.
Retries: API errors (429, 413, timeouts) are not retried by the app. The provider chain moves on to the next model instead. Malformed JSON or a wrong output shape is retried exactly once with a "return only valid JSON" reminder (models.py).
Provider fallback chain: Groq openai/gpt-oss-120b → qwen/qwen3.8-27b → openai/gpt-oss-20b → Mistral mistral-small-latest. Each model has a 30s timeout, and SDK retries are off. The primary Groq model and the Mistral model can be overridden with GROQ_MODEL / MISTRAL_MODEL.
Free-tier pacing: to stay under Groq's 8,000 tokens-per-minute limit, the pipeline sleeps 35s before Verifier, 30s before Analyst, and 50s before Editor. Set SKIP_PIPELINE_PAUSE=1 to disable (pauses are skipped automatically under pytest).
Four LLM calls per run: Skeptic, Verifier, Analyst, and Editor make one call each, plus a retry if the JSON was malformed.
Prompt-injection hardening: scraped text is wrapped in tags the model is told to treat as data. HTML comments are stripped and < / > are escaped (escape_untrusted).
Publish gate rules (per brief, checks.py):
1. All required fields present: category, title, description, why_it_matters, who_cares, verdict, source_url.
2. Category is one of the 5 designated categories; verdict is WATCH, INTEGRATE, or READ.
3. Description starts with a bold sentence (**...**) and contains no TL;DR: / TLDR: label.
4. Source URL is HTTPS and matches a researched candidate (ignoring utm_* params and trailing slashes).
5. No generic filler text.
6. Every version, multiplier (10x), percentage, k/m/b count, or N+ figure in the title, description, or why_it_matters appears in the source evidence (title + description + scraped text).
Run-level decision: briefs that fail are dropped, duplicate categories are removed, and at most 5 are kept. Publication is blocked (degraded snapshot, exit code 1) if any node failed, if every brief was dropped, or if fewer than MIN_BRIEFS (default 3) survive. If the pipeline produced nothing and nothing errored, the run is treated as a quiet day and an empty edition is recorded.

Same-day retries: the workflow runs at 09:00 IST, then again at 11:00 and 13:00 IST. Each run first checks whether today's edition is already published, and stops if it is (or only resends a pending email). Progress is saved per step in data/runs/state/YYYY-MM-DD.json, so a retry continues where the last run stopped:

What happened	Next run starts at
API or bad-JSON error in a step	That step (finished steps are reused)
Verifier approved fewer than `MIN_BRIEFS` items	Scout, with today's rejected URLs skipped by the Skeptic
Analyst kept fewer than `MIN_BRIEFS` items	Analyst
Gate kept fewer than `MIN_BRIEFS` briefs	Editor

On a restart, a Verifier that finds nothing new counts as a failure, not a quiet day. If the 13:00 run also fails, no edition is sent that day.

Email delivery: before notifying subscribers, the runner wakes the backend (GET /api/status every 10s, for up to 120s, to cover Render's cold start). Only HTTP 200 from /api/notify-subscribers counts as sent. Otherwise the email stays pending in the state file, the run exits 1, and the next retry resends it without re-running the pipeline. "Sent" means the backend accepted the request; delivery failures inside the backend (Brevo/SMTP) are not visible to the pipeline.
Audit snapshots: every run writes data/runs/YYYY-MM-DD.json (or .degraded.json) with stage counts, content sources, models used, LLM call count, node durations, hard failures, warnings, and the final briefs. CI commits data/ even when the run fails.

📊 Live Evaluation Harness & Results

The evaluation harness in tests/evals/ makes live LLM calls against two hand-built datasets. Rates come with Wilson score 95% confidence intervals.

golden_dataset.json: 25 cases (18 expected KEEP, 7 expected REJECT), 15 of them with an expected Analyst verdict.
planted_errors.json: 13 traps (2 exaggerations, 2 wrong numbers, 2 unsupported features, 2 empty-evidence cases, 5 prompt injections).
G-Eval Usefulness and Faithfulness scores come from an uncalibrated LLM judge (judge.py) that uses the same provider chain.

Latest Recorded Run

Important

The stored results (tests/evals/results/latest.json) were recorded on 2026-10-15. That is before the Groq model switch (Oct 19) and the Skeptic/Researcher token budgets (Oct 21), so they do not reflect the current configuration.

================================================================================
Node         | Metric                         | Count & Rate
--------------------------------------------------------------------------------
Skeptic      | Accuracy                       | 16/25 = 64% (CI: roughly 45–80%)
Skeptic      | Always-KEEP Baseline           | 18/25 = 72% (CI: roughly 52–86%)
Skeptic      | Precision                      | 10/11 = 91% (CI: roughly 62–98%)
Skeptic      | Recall                         | 10/18 = 56% (CI: roughly 34–75%)
Skeptic      | Scope Drift Filtered           | 6/7 = 86%   (CI: roughly 49–97%)
Verifier     | Bad Items Caught               | 14/16 = 88% (CI: roughly 64–97%)
Verifier     | Good Items Wrongly Rejected    | 5/22 = 23%  (CI: roughly 10–43%)
Verifier     | Prompt Injections Succeeded    | 2/5 = 40%
Analyst      | Verdict Match (Ref-based)      | 13/15 = 87% (CI: roughly 62–96%)
Analyst      | G-Eval Usefulness Avg          | 0.49 / 1.0  (>= 0.7: 3/14 = 21%)
Editor       | Rule Checks Passed             | 5/5 briefs on every rule
Editor       | G-Eval Faithfulness Avg        | 0.96 / 1.0
Pipeline     | Briefs Produced                | 0 of 12 frozen inputs
Pipeline     | Bad Items Leaked               | 0/6
Pipeline     | Gate Decision                  | WOULD PUBLISH (quiet-day path, 0 briefs)
Pipeline     | Total Duration                 | 4.16s (Skeptic + Verifier only)
================================================================================

Note

In that run the end-to-end pipeline eval produced no briefs: the Verifier passed none of the Skeptic's picks, so Analyst and Editor never ran. "0/6 leaked" and "WOULD PUBLISH" are therefore trivially true for that run. The Editor numbers come from the separate Editor eval, which feeds 5 known-good golden cases through the real Analyst and Editor.

Prompt Engineering Progression

Taken from the stored runs before_prompts.json, after_prompts.json, and latest.json:

Metric	Baseline (Oct 14)	Prompts Restored (Oct 15)	Latest (Oct 15)
Skeptic Precision	76% (13/17)	100% (14/14)	91% (10/11)
Skeptic Scope Drift Filtered	43% (3/7)	100% (7/7)	86% (6/7)
Verifier Bad Claims Caught	94% (15/16)	94% (15/16)	88% (14/16)
Verifier Prompt Injections Succeeded	1/5	1/5	2/5
Analyst Verdict Match	93% (14/15)	87% (13/15)	87% (13/15)
Editor Numeric Grounding	60% (3/5)	60% (3/5)	100% (5/5)
Editor G-Eval Faithfulness	0.54	0.64	0.96
Pipeline Gate Decision	`BLOCKED` (5 briefs)	`BLOCKED` (4 briefs)	`WOULD PUBLISH` (0 briefs)

Note

Why honest metrics matter: real open-weights models make mistakes. An evaluation suite claiming "100% pass rates" on prompt injections and fact-checking suggests simulated or seeded data. tests/test_no_fake_results.py fails the build if simulation patterns appear in the repo.

🚀 Quick Start & Local Setup

1. Prerequisites

Python 3.12+
Node.js 18+ & npm
A free API key from Groq Console and/or Mistral AI. At least one is required.

2. Installation

git clone https://github.com/shlokbam/DailyDiff.git
cd DailyDiff

# Set up Python virtual environment
python3.12 -m venv .venv
source .venv/bin/activate

# Install backend dependencies (pytest is installed separately)
pip install -r backend/requirements.txt pytest

# Install frontend dependencies
cd frontend && npm install && cd ..

3. Environment Configuration

Create a .env file in the project root (loaded by backend/app/config.py):

# LLM providers: at least one key is required (chain: Groq models -> Mistral)
GROQ_API_KEY=gsk_your_groq_api_key_here
MISTRAL_API_KEY=your_mistral_api_key_here
# Optional model overrides
# GROQ_MODEL=openai/gpt-oss-120b
# MISTRAL_MODEL=mistral-small-latest

# Optional: raises GitHub API rate limits for Scout and Researcher
GITHUB_TOKEN=your_github_token

# Subscriber database: leave UNSET to use local SQLite (backend/subscribers.db).
# When set, it must be a PostgreSQL connection string (e.g. Neon).
# DATABASE_URL=postgresql://user:password@host/dbname

# Subscriber notification (run_agent.py -> FastAPI)
BACKEND_API_URL=http://localhost:8000
NOTIFY_SECRET_TOKEN=your_secure_secret_token

# Optional: email delivery, tried in this order: Brevo -> Gmail SMTP -> Resend.
# If none succeeds, the email HTML is written to logs/.
BREVO_API_KEY=your_brevo_api_key
SMTP_EMAIL=your_email@gmail.com
SMTP_PASSWORD=your_app_password
RESEND_API_KEY=your_resend_api_key

# Optional: pipeline tuning
# MIN_BRIEFS=3
# SKIP_PIPELINE_PAUSE=1

# Optional: LangSmith tracing
# LANGCHAIN_TRACING_V2=true
# LANGCHAIN_API_KEY=your_langsmith_key
# LANGCHAIN_PROJECT=DailyDiff

4. Running Offline Unit Tests

75 offline tests cover the rule checks, the publish gate, retry & resume, email retry, fail-closed node behavior, JSON retry logic, prompt escaping, feed parsing, API auth, and the anti-simulation guard. No live API calls are made:

PYTHONPATH=backend pytest tests -m "not live" -q

5. Running the Pipeline Locally

PYTHONPATH=backend python backend/run_agent.py

This writes to data/ (run snapshot, history.json, archive markdown). It notifies subscribers only if BACKEND_API_URL is set and the gate passes.

6. Running the Live Evaluation Benchmark

python tests/evals/run_evals.py

Run it from the repository root. Results are written to tests/evals/results/run_<timestamp>.json and latest.json, and mirrored, together with both datasets, into frontend/public/. The run exits with code 1 if any case errored, which is likely on Groq's free tier because of rate limits.

7. Starting the Web Application

🚀 Production: the frontend is live at https://dailydiff.in, backed by the API at https://dailydiff.onrender.com.

Backend API (FastAPI):

export PYTHONPATH=backend
uvicorn app.main:app --reload --port 8000

Method	Endpoint	Purpose
`GET`/`HEAD`	`/api/status`	Health check
`POST`	`/api/subscribe`	Add a subscriber (`{"email": ...}`)
`POST`	`/api/unsubscribe`	Remove a subscriber and send a confirmation email
`GET`	`/api/unsubscribe?email=...`	Unsubscribe via link (returns an HTML page)
`GET`/`HEAD`	`/api/briefs/latest`	Most recent edition
`GET`	`/api/briefs/archive?page=1&limit=10`	Editions, newest first
`GET`	`/api/briefs/{YYYY-MM-DD}`	Edition for a date
`POST`	`/api/notify-subscribers`	Email all subscribers (requires `X-Auth-Token: $NOTIFY_SECRET_TOKEN`)

Editions are read from data/history.json; the database stores subscribers only.

Frontend (React + Vite):

cd frontend
npm run dev

Open http://localhost:5173/. On localhost the app calls http://localhost:8000/api; elsewhere it calls the Render backend. It first renders from the static frontend/public/data/history.json, then merges in editions from the live API. The How It's Made tab explains the architecture, and the Evaluations tab shows the scorecard and case inspectors from frontend/public/eval_results.json.

⚙️ CI/CD

Workflow	Trigger	What it does
`thrice_weekly_brief.yml`	Cron `30 3`, `30 5`, `30 7 * * 1,3,5` (09:00, 11:00, 13:00 IST, Mon/Wed/Fri) and manual dispatch	Installs deps with `uv`, runs `backend/run_agent.py`, then commits and pushes any changes under `data/` (including the day's state file), even when the run is blocked. The two later runs exit immediately once the day is done.
`evals.yml`	Pull requests	Runs the offline test suite (`pytest tests -m "not live"`).

📂 Project Structure

DailyDiff/
├── .github/workflows/
│   ├── thrice_weekly_brief.yml  # Scheduled pipeline run (Mon, Wed, Fri) + data commit
│   └── evals.yml                # Offline test suite on pull requests
├── backend/
│   ├── run_agent.py             # Pipeline runner, resume/retry rules, publish gate, email delivery
│   ├── test_api.py              # Legacy unittest API checks (not collected by pytest)
│   ├── requirements.txt         # Python dependencies
│   └── app/
│       ├── config.py            # .env loading, paths, model defaults, LangSmith env
│       ├── database.py          # SQLite / Postgres subscribers + history.json I/O
│       ├── checks.py            # Per-brief rule checks & numeric grounding
│       ├── run_health.py        # Run telemetry: counts, timings, failures, warnings
│       ├── run_state.py         # Per-day progress file for resume & email retry
│       ├── schemas.py           # Pydantic request/response models
│       ├── email_dispatcher.py  # HTML email builder + Brevo/SMTP/Resend delivery
│       ├── main.py              # FastAPI endpoints
│       └── agents/
│           ├── graph.py         # Linear LangGraph StateGraph
│           ├── state.py         # AgentState TypedDict
│           ├── models.py        # Provider chain, JSON retry, pacing, input escaping
│           ├── scout.py         # Feed / API signal collection
│           ├── skeptic.py       # History dedup + LLM relevance filter
│           ├── researcher.py    # README / web page evidence scraper (1,800 chars)
│           ├── verifier.py      # Batched fact-check
│           ├── analyst.py       # Batched why / who / verdict
│           └── editor.py        # Batched brief writing & category assignment
├── frontend/
│   ├── src/
│   │   ├── App.jsx              # Briefing dashboard, subscribe/unsubscribe, edition picker
│   │   ├── index.css            # Global styles
│   │   └── components/
│   │       ├── AgentEvaluations.jsx  # Evaluation scorecard & case inspector
│   │       ├── TechnicalDetails.jsx  # "How It's Made" architecture page
│   │       └── OnboardingCalendar.jsx # Dispatch schedule & countdown
│   └── public/
│       ├── data/history.json    # Static history cache for first render
│       ├── eval_results.json    # Mirrored latest eval results
│       ├── golden_dataset.json  # Mirrored eval dataset
│       └── planted_errors.json  # Mirrored eval dataset
├── tests/
│   ├── conftest.py              # Forces local SQLite, sets import paths
│   ├── test_checks.py           # Rule check unit tests
│   ├── test_gate.py             # Publish gate & quiet-day tests
│   ├── test_resume.py           # Retry & resume, email wake/retry
│   ├── test_run_state.py        # Daily state file
│   ├── test_nodes.py            # Fail-closed nodes, JSON retry, escaping
│   ├── test_scout_parsing.py    # RSS/Atom & GitHub parsing tests
│   ├── test_api_auth.py         # Notify-endpoint auth & unsubscribe tests
│   ├── test_no_fake_results.py  # Anti-simulation regression guard
│   └── evals/
│       ├── golden_dataset.json  # 25 ground-truth scenarios
│       ├── planted_errors.json  # 13 planted traps (5 prompt injections)
│       ├── run_evals.py         # Runs all evals, saves & mirrors results
│       ├── judge.py             # DeepEval G-Eval judge (Usefulness, Faithfulness)
│       ├── common.py            # Loaders, Wilson intervals, result comparison
│       ├── eval_*.py            # Skeptic, Verifier, Analyst, Editor, Pipeline, History
│       └── results/             # Stored eval runs (latest.json, before/after prompts)
├── data/
│   ├── history.json             # All published editions
│   ├── archive/YYYY/MM/DD.md    # Markdown copy of each published edition
│   └── runs/                    # Per-run snapshots (*.json / *.degraded.json)
│       └── state/               # Per-day progress: stage outputs, rejected URLs, email status
├── SYSTEM_ARCHITECTURE_AND_DECISION_ENGINE.md
├── FREE_TIER_LLM_INCIDENT_REPORT_AND_ANALYSIS.md
└── README.md

📄 License

MIT License.

Built by Shlok Bam, see more projects on my portfolio.

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DailyDiff ⚡

🌟 Why DailyDiff?

🏗️ System Architecture

Pipeline Nodes

🛡️ Reliability & Fault-Tolerance Principles

📊 Live Evaluation Harness & Results

Latest Recorded Run

Prompt Engineering Progression

🚀 Quick Start & Local Setup

1. Prerequisites

2. Installation

3. Environment Configuration

4. Running Offline Unit Tests

5. Running the Pipeline Locally

6. Running the Live Evaluation Benchmark

7. Starting the Web Application

⚙️ CI/CD

📂 Project Structure

📄 License

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Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
backend		backend
data		data
docs/superpowers		docs/superpowers
frontend		frontend
tests		tests
.gitignore		.gitignore
.python-version		.python-version
FREE_TIER_LLM_INCIDENT_REPORT_AND_ANALYSIS.md		FREE_TIER_LLM_INCIDENT_REPORT_AND_ANALYSIS.md
README.md		README.md
SYSTEM_ARCHITECTURE_AND_DECISION_ENGINE.md		SYSTEM_ARCHITECTURE_AND_DECISION_ENGINE.md
pytest.ini		pytest.ini

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DailyDiff ⚡

🌟 Why DailyDiff?

🏗️ System Architecture

Pipeline Nodes

🛡️ Reliability & Fault-Tolerance Principles

📊 Live Evaluation Harness & Results

Latest Recorded Run

Prompt Engineering Progression

🚀 Quick Start & Local Setup

1. Prerequisites

2. Installation

3. Environment Configuration

4. Running Offline Unit Tests

5. Running the Pipeline Locally

6. Running the Live Evaluation Benchmark

7. Starting the Web Application

⚙️ CI/CD

📂 Project Structure

📄 License

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