Franck MIGONE

1.1K posts

Franck MIGONE

Franck MIGONE

@fajmigone

Research to propel Development | Public Policy & Poverty & Climate & Labor | Machine Learning

Abidjan Cocody Katılım Kasım 2015
334 Takip Edilen129 Takipçiler
TREIIZIVOIR
TREIIZIVOIR@225ivoir·
🇨🇮🎙️⏩️ || Un créateur de contenu venant d’Azerbaïdjan 🇦🇿 a filmé une scène exposant des agents identifiés comme Douaniers tentant de lui soutirer de l’argent à l’Aéroport de d’Abidjan 🇨🇮 Le motif il avait 2000$ et 2000€ en cash sur lui en direction de Conakry. Selon les individus épinglés un non résident ne peut passer l’aéroport avec un montant n’excédant pas 500.000 FCFA en cash. Ils ont donc essayé de lui prendre 10% du montant total soit environ 400€ chose qu’il a refusé. Il leur a finalement remis 10$ C’est grave ce qui se passe !
TREIIZIVOIR@225ivoir

Un Virgile apperçu rackettant un touriste à la cathédrale du plateau. Il voulait 2000 FCFA

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Joachim Schork
Joachim Schork@JoachimSchork·
Are you creating your plots and statistical tests separately? This is a common workflow in data analysis, but it can be inefficient and error prone because your visualizations and statistical results are disconnected. ❌ You need to run tests manually and copy the results into your plots. ❌ Updating your analysis requires repeating multiple steps. The ggstatsplot package solves this problem by combining visualization and statistical analysis in a single step. The visualization below shows an example. It displays the distribution of prices across groups and automatically includes the corresponding statistical test results, effect sizes, confidence intervals, and pairwise comparisons directly in the plot. This allows you to see both the data and the statistical conclusions in one place. This approach saves time, reduces errors, and makes your analysis more transparent. In an upcoming module of the Statistics Globe Hub, I will show step by step how to create informative visualizations with ggstatsplot in R, how to include statistical results automatically, and how to use this workflow in real projects. Haven’t heard about the Statistics Globe Hub yet? It’s my new continuous learning program with weekly modules on statistics, data science, AI, and programming in R and Python. It starts on March 2, and members get access to a new practical module every week. More info: statisticsglobe.com/hub #Statistics #DataScience #RStats #DataVisualization #ggstatsplot
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Hasan Toor
Hasan Toor@hasantoxr·
🚨BREAKING: Someone compiled a collection of every production-ready LLM app you can build in 2026. It's called awesome-llm-apps and it's literally copy-paste code for RAG, agents, multimodal apps, and AI SaaS products. → Need RAG? Copy the code. → Need AI agents? Copy the code. → Need multimodal apps? Copy the code. No "hello world" tutorials. No beginner demos. Just real applications you can deploy today. 100% free. 100% Open Source.
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cherrie
cherrie@cherrishkhera·
every time i try to have an original research idea but Zhang et al. already published it 3 years ago
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Muhammad Muneeb
Muhammad Muneeb@im2muneeb·
How to Read a Research Paper Quickly and Effectively? One of the most important skills a young researcher need is to learn is how to read a paper. Here is a very useful article you must read to learn this very important skill. #phd
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Franck MIGONE
Franck MIGONE@fajmigone·
@abhi1thakur Oh great. We are building a local RAG for Work Documents. What would be the best way to scale in productions.
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abhishek
abhishek@abhi1thakur·
Most people think RAG is dumping a few PDFs in a chat app and talking to them. Now, please dump a million docs or even a few hundred thousand docs in your favorite chat app and tell me the results!
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Franck MIGONE
Franck MIGONE@fajmigone·
Je pense qu'on peut build un Podcast style NotebookLM local tranquillement avec ça. Un Qwen open source pour résumer l'article avec cette injonction de la transformer en discussion entre deux personnes et PersonaPlex-7B pour l'audio
Charly Wargnier@DataChaz

NVIDIA just removed one of the biggest friction points in Voice AI. PersonaPlex-7B is an open-source, full-duplex conversational model. Free, open source (MIT), with open model weights on @huggingface 🤗 Links to repo and weights in 🧵↓ The traditional ASR → LLM → TTS pipeline forces rigid turn-taking. It’s efficient, but it never feels natural. PersonaPlex-7B changes that. This @nvidia model can listen and speak at the same time. It runs directly on continuous audio tokens with a dual-stream transformer, generating text and audio in parallel instead of passing control between components. That unlocks: → instant back-channel responses → interruptions that feel human → real conversational rhythm Persona control is fully zero-shot! If you’re building low-latency assistants or support agents, this is a big step forward 🔥

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Sumanth
Sumanth@Sumanth_077·
Document Index for Vectorless, Reasoning-based RAG! PageIndex is an open-source RAG framework that removes vector databases and chunking from document retrieval. Most RAG systems rely on semantic similarity. They chunk documents arbitrarily, embed them into vectors, and retrieve based on what looks similar. But similarity ≠ relevance. Professional documents like financial reports, legal filings, and technical manuals require multi-step reasoning and domain expertise. Vector search falls short when every section contains similar terminology. PageIndex takes a different approach. It builds a hierarchical tree structure from documents, similar to a table of contents but optimized for LLMs. Then it uses reasoning-based tree search to navigate and retrieve information the way human experts would. Two-step process: Generate a tree structure index of the document → Perform reasoning-based retrieval through tree search. The LLM can "think" about document structure. Instead of matching embeddings, it reasons: "Debt trends are usually in the financial summary or Appendix G, let's look there." Key features: • No vector database infrastructure or embedding pipelines • No artificial chunking that breaks context across boundaries • Traceable retrieval with exact page-level references • Reasoning-based navigation that mirrors human document analysis PageIndex powers Mafin 2.5, achieving 98.7% accuracy on FinanceBench for financial document analysis. The best part? It's 100% open source. Link to the GitHub repo in the comments!
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Franck MIGONE
Franck MIGONE@fajmigone·
God of Prompt@godofprompt

Vibe coding without this prompt is a waste of time. -------------------------------- LEAD SOFTWARE ARCHITECT -------------------------------- You are my lead software architect and full-stack engineer. You are responsible for building and maintaining a production-grade app that adheres to a strict custom architecture defined below. Your goal is to deeply understand and follow the structure, naming conventions, and separation of concerns. Every generated file, function, and feature must be consistent with the architecture and production-ready standards. Before writing ANY code: read the ARCHITECTURE, understand where the new code fits, and state your reasoning. If something conflicts with the architecture, stop and ask. --- ARCHITECTURE: [ARCHITECTURE] TECH STACK: [TECH_STACK] PROJECT & CURRENT TASK: [PROJECT] CODING STANDARDS: [STANDARDS] --- RESPONSIBILITIES: 1. CODE GENERATION & ORGANIZATION • Create files ONLY in correct directories per architecture (e.g., /backend/src/api/ for controllers, /frontend/src/components/ for UI, /common/types/ for shared models) • Maintain strict separation between frontend, backend, and shared code • Use only technologies defined in the architecture • Follow naming conventions: camelCase functions, PascalCase components, kebab-case files • Every function must be fully typed — no implicit any 2. CONTEXT-AWARE DEVELOPMENT • Before generating code, read and interpret the relevant architecture section • Infer dependencies between layers (how frontend/services consume backend/api endpoints) • When adding features, describe where they fit in architecture and why • Cross-reference existing patterns before creating new ones • If request conflicts with architecture, STOP and ask for clarification 3. DOCUMENTATION & SCALABILITY • Update ARCHITECTURE when structural changes occur • Auto-generate docstrings, type definitions, and comments following existing format • Suggest improvements that enhance maintainability without breaking architecture • Document technical debt directly in code comments 4. TESTING & QUALITY • Generate matching test files in /tests/ for every module • Use appropriate frameworks (Jest, Vitest, Pytest) and quality tools (ESLint, Prettier) • Maintain strict type coverage and linting standards • Include unit tests and integration tests for critical paths 5. SECURITY & RELIABILITY • Implement secure auth (JWT, OAuth2) and encryption (TLS, AES-256) • Include robust error handling, input validation, and logging • NEVER hardcode secrets — use environment variables • Sanitize all user inputs, implement rate limiting 6. INFRASTRUCTURE & DEPLOYMENT • Generate Dockerfiles, CI/CD configs per /scripts/ and /.github/ conventions • Ensure reproducible, documented deployments • Include health checks and monitoring hooks 7. ROADMAP INTEGRATION • Annotate potential debt and optimizations for future developers • Flag breaking changes before implementing --- RULES: NEVER: • Modify code outside the explicit request • Install packages without explaining why • Create duplicate code — find existing solutions first • Skip types or error handling • Generate code without stating target directory first • Assume — ask if unclear ALWAYS: • Read architecture before writing code • State filepath and reasoning BEFORE creating files • Show dependencies and consumers • Include comprehensive types and comments • Suggest relevant tests after implementation • Prefer composition over inheritance • Keep functions small and single-purpose --- OUTPUT FORMAT: When creating files: 📁 [filepath] Purpose: [one line] Depends on: [imports] Used by: [consumers] ```[language] [fully typed, documented code] ``` Tests: [what to test] When architecture changes needed: ⚠️ ARCHITECTURE UPDATE What: [change] Why: [reason] Impact: [consequences] --- Now read the architecture and help me build. If anything is unclear, ask before coding.

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Mickipamickey
Mickipamickey@mickipamickey·
@Gil_225 @ThePapin_93 @AfricaFirsts 🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤭🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣
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Africa First
Africa First@AfricaFirsts·
Cocody is the most affluent district in Ivory Coast (Côte d'Ivoire) 🇨🇮.
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Elvis
Elvis@elvissun·
run this in background to never hit the 5hr limit. thank me later.
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Franck MIGONE
Franck MIGONE@fajmigone·
@acagamic Turn papiers into podcast via NoteBookLM is becoming my must.
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Prof Lennart Nacke, PhD
Prof Lennart Nacke, PhD@acagamic·
7 AI tools I actually use for literature reviews: 1. Elicit – paper screening 2. Consensus – evidence summaries 3. SciSpace – chat with PDFs 4. Litmaps – citation mapping 5. Perplexity – quick context 6. Claude – synthesis drafts 7. Zotero – still the citation king with AI plugins Speed matters when you're reading 200 papers.
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