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MAGet past some paywalls with this trick
@mattupham
#Trusted Platform Module Tpm Explained
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141.3K
PRFrameworks aren’t just buzzwords. They’re your cheat codes 💜
Here are 20 PM frameworks I reference every single week. Bookmark this - you’ll come back to it more than you think.
Stop guessing. Start using frameworks 👇
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🎯 PRIORITIZATION:
1. RICE Score → Reach, Impact, Confidence, Effort - Score features objectively
2. MoSCoW Method → Must have, Should have, Could have, Won’t have
3. Kano Model → Basic, Performance, Excitement features
4. Value vs Effort Matrix → Quick wins, big bets, fill-ins, time sinks
📊 STRATEGY:
5. Jobs-to-be-Done (JTBD) → What “job” is your user hiring your product for?
6. North Star Metric → The ONE metric that matters most
7. OKRs → Objectives and Key Results for measurable outcomes
8. Product-Market Fit → Are you solving a real problem people will pay for?
🧠 DISCOVERY:
9. The Five Whys → Keep asking “why” to find root causes
10. Empathy Mapping → What users say, think, do, and feel
11. User Story Mapping → Visualize the complete user journey
12. Opportunity Solution Tree → Connect outcomes to opportunities
🚀 DEVELOPMENT:
13. MVP Framework → Ship fast, learn faster
14. Build-Measure-Learn → The lean startup cycle
15. Story Points & Velocity → Estimate and track team capacity
📈 GROWTH:
16. AARRR (Pirate Metrics) → Acquisition, Activation, Retention, Referral, Revenue
17. Retention Curves → Visualize user stickiness over time
18. NPS Framework → Measure user satisfaction and loyalty
💡 DECISION-MAKING:
19. Pre-Mortem Analysis → Imagine your project failed. Why?
20. ICE Score → Impact, Confidence, Ease for quick prioritization
—
🎁 Want FREE templates? Link in bio has:
→ Complete frameworks guide (PDF)
→ RICE Score calculator (Excel)
→ OKR planning template (Excel)
→ Quick reference cheat sheet
All ready to download and use today!
Which framework are you using this week? Drop the number 👇
Save this. Reference it. Master it 💜
#ProductWithSai #InsteadOfScrollingSundays #ProductManagement #PMFrameworks #ProductStrategy #AspiringPM #ProductManager #PMTips #LearnPM #CareerTransition
@productwithsai
318.3K
CHBoth are used to control the traffic and protect the system but at different layers.
• Rate limiting → User level control
• Throttling → System level control
Comment “pdf” for detailed notes.
1️⃣ Rate Limiting (User / Client level)
Limit how many requests one user / API key / IP can make in a time window.
If user crosses limit → request is rejected ❌
📌 Applied at:
API Gateway / Load balancer / Auth layer
2️⃣ Throttling (System level)
Control traffic when the system itself is under pressure.
• System slows down or temporarily blocks requests
• Decision is based on CPU, memory, DB load, queue size
📌 Applied at:
Service layer / Queue / Thread pool / DB connections
3️⃣ When to Use What
👉 Rate limit → Stop bots, scrapers, brute-force attacks.
👉 Throttle → Handle traffic spikes, bursty clients, peak hours.
Most of the systems use both.
#systemdesign #tech #interview #backenddeveloper #growth
(API rate limiting, API throttling, backend system design, prevent API abuse, scalable APIs, system design interview, backend engineering, distributed systems)
@chhavi_maheshwari_
5.9M
PEWindows Task Manager lies about high CPU usage. Hidden Microsoft tracking services like telemetry and diatrack secretly eat up 40% of your CPU power, disguised under system names. Learn how to disable these unnecessary windows services (Connected User Experiences, Compatibility Appraiser) that kill your PC performance. Stop Microsoft spying and reclaim your computer speed instantly!
We make content and explaining complex tech features simply in our videos👍🏻
If you want content on a particular topic let us know in the comments
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Follow or you may never see us again...
Follow @petercodelogic for more
Follow @petercodelogic for more
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#familyguy #thefamilyguy #explanation #explanations #techexplained #techniques #techrevolution #windows #microsoft #cpuoptimization #telemetry #privacy #computertips #techhacks #pctips #taskmanager #windows11 #performance
@petercodelogic
345.8K
TE1. The “Cost-Control” Smart Model Router (The Infrastructure Project)
The Problem: GPT-4o is brilliant but costs a fortune. Llama 3 8B is cheap but can’t handle complex reasoning. Companies are burning money using the most expensive model for every task.
The Full-Stack Solution:
1. Backend: A single, custom API endpoint built with FastAPI. This is the only thing your “user” (another developer) talks to.
2. AI Core: This is the magic. When a prompt arrives at your API, it’s first sent to a tiny, fast classifier model (like a fine-tuned Phi-3). This “router agent” classifies the prompt’s difficulty (e.g., simple_summarization, complex_code_gen). 3. Based on that tag, your API backend routes the prompt to the right model: Phi-3 for simple, Llama 3 70B for medium, and GPT-4o for hard.
4. Data/Infra: A simple database (like PostgreSQL) to log every request, the “difficulty” tag, and the cost saved.
_____
2. The “RAG Pipeline Optimizer” (The MLOps Project)
The Problem: Every company has a RAG (Retrieval-Augmented Generation) system. Almost no company knows if their RAG is good. Is chunk_size=512 better than 1024? Is Cohere-v3 a better embedder than BERT for their data? They’re just guessing.
The Full-Stack Solution:
1. Frontend: A Streamlit or React app where a user uploads their documents (e.g., all of a company’s HR policies).
2. Backend: A FastAPI server. When the user asks a question, the backend runs 4 RAG pipelines in parallel with different chunking/embedding/reranking strategies.
3. AI Core: An “evaluator” agent (using GPT-4o as a judge) that scores the outputs from all 4 pipelines on “accuracy,” “relevance,” and “cost.”
4. Data/Infra: A dashboard that visualizes the results, showing the user: “For your data, Pipeline C is 20% more accurate and 10% cheaper.” Deployed with Docker on Render.
____
(Follow for more such ideas)
3. The “Autonomous Codebase Documenter” (The AI Agent Project)
The Problem: Every software company on earth has a massive, complex codebase with zero documentation. New engineers take 6 months to become productive because they don’t know what anything does.
1. Frontend…. (Instagram caption has a word limit, continued in the comments 🥹)
@techwithnt
13.3M
TE🫵🏻Apka UpTime kitna hai?😆
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Procedure to check:
1. Open Task Manager and check CPU up time to verify PC activity duration.
2. Go to Control Panel and search for power settings.
3. Click on the first option and navigate to change settings.
4. Untick Fast Setup to adjust power settings.
5. Save changes to ensure correct shutdown behaviour
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#windows #pc #tech #techno #technology #techiela #techreels #techvideos #tipsandtricks #tips #trick #pcgaming #laptop #laptops #reels #reelitfeelit #reelsinstagram #techie #windowshacks #windowstips #windowstipsandtricks #windows11 #pchacks
@tech_iela
25.1K
MEOffice TPM :- Production losses often start in the office, not on the shop floor.
#tpm #electrical #engineer #vmc #hmc
@mechnode
22.1K
SKFree FRP tool for mtk 🔥
#tool #frptool #mobilerepair #mobilerepairing
@skprepairs_
34.4K
TEPowerfull TERMUX tool 🔥 ⚡...............
#cybersecurity #programminglanguage #hacking #cyber #roadmap #algorithms #termux #termuxhacking #andromeda
@tech_moris
132.9K
PY5 Platforms to Find Datasets for Your Machine Learning Projects
👉 | Kaggle: offers thousands of real-world datasets with notebooks and competitions for hands-on practice.
👉 | UCI: Machine Learning Repository provides classic, clean tabular datasets used in research and courses.
👉 | OpenML: hosts datasets linked to benchmarks and experiments, great for model comparisons.
👉 | Google Dataset Search: indexes millions of datasets from governments and repos by topic.
👉 | GitHub: curates open datasets via "awesome-public-datasets" lists across all domains.
#MachineLearning #Datasets #DataScience #MLProjects #python3
@pycode.hubb
3.6K
9E📌10 ความสามารถเด็ด‼️ ของ Power Automate Desktop สร้างเวิร์คโฟลด์อัตโนมัติ ช่วยงานเสร็จไว🔺ขึ้น ซอฟต์แวร์ #RPA #Microsoft #PowerAutomate #AI #9Expert
@9expert_training
529.3K
EQThe Secret to Perfect Data Models #MachineLearning #PolynomialRegression #Statistics #Math #Manim Ever wondered why your machine learning model isn't performing as expected? In this video, we break down polynomial curve fitting, a fundamental concept in data science and statistics. We explore the visual differences between Degree 1 (Underfitting), Degree 3 (Good Fit), and Degree 11 (Overfitting). Learn how increasing the degree of a polynomial affects how it captures data trends and why the optimal model is crucial for accurate predictions.
@equationsinmotion
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