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๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐ง๐ผ ๐๐ฒ๐ ๐ง๐ฒ๐ฐ๐ต ๐๐ผ๐ฏ ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
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Here are some essential SQL tips for beginners ๐๐
โ Primary Key = Unique Key + Not Null constraint
โ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE โA%Aโ
โ LIKE operator is for string data type
โ COUNT(*), COUNT(1), COUNT(0) all are same
โ All aggregate functions ignore the NULL values
โ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
โ For row level filtration use WHERE and aggregate level filtration use HAVING
โ UNION ALL will include duplicates where as UNION excludes duplicates
โ If the results will not have any duplicates, use UNION ALL instead of UNION
โ We have to alias the subquery if we are using the columns in the outer select query
โ Subqueries can be used as output with NOT IN condition.
โ CTEs look better than subqueries. Performance wise both are same.
โ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
โ Window functions work at ROW level.
โ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
โ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.
Like for more ๐๐
โ Primary Key = Unique Key + Not Null constraint
โ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE โA%Aโ
โ LIKE operator is for string data type
โ COUNT(*), COUNT(1), COUNT(0) all are same
โ All aggregate functions ignore the NULL values
โ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
โ For row level filtration use WHERE and aggregate level filtration use HAVING
โ UNION ALL will include duplicates where as UNION excludes duplicates
โ If the results will not have any duplicates, use UNION ALL instead of UNION
โ We have to alias the subquery if we are using the columns in the outer select query
โ Subqueries can be used as output with NOT IN condition.
โ CTEs look better than subqueries. Performance wise both are same.
โ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
โ Window functions work at ROW level.
โ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
โ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.
Like for more ๐๐
โค1
๐ช๐ฎ๐ป๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ง๐ต๐ฎ๐ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐ ๐๐ฟ๐ฒ ๐๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ผ๐ฟ?๐
If youโre looking to land a job in tech or simply want to upskill without spending money, this is your golden chanceโจ๏ธ๐
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Hereโs your roadmap โ pick one, stay consistent, and grow dailyโ ๏ธ
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Guide to Building an AI Agent
1๏ธโฃ ๐๐ต๐ผ๐ผ๐๐ฒ ๐๐ต๐ฒ ๐ฅ๐ถ๐ด๐ต๐ ๐๐๐
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses
๐ Tip: Experiment with models & fine-tune prompts to enhance reasoning.
2๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐๐ผ๐ด๐ถ๐ฐ
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.
๐ Choosing the right approach improves reasoning & reliability.
3๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ผ๐ฟ๐ฒ ๐๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป๐ & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ๐
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?
๐ Clear system prompts shape agent behavior.
4๏ธโฃ ๐๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐ ๐ฎ ๐ ๐ฒ๐บ๐ผ๐ฟ๐ ๐ฆ๐๐ฟ๐ฎ๐๐ฒ๐ด๐
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.
๐ Example: A financial AI recalls risk tolerance from past chats.
5๏ธโฃ ๐๐พ๐๐ถ๐ฝ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐ ๐๐ถ๐๐ต ๐ง๐ผ๐ผ๐น๐ & ๐๐ฃ๐๐
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Description: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?
๐ Example: A support AI retrieves order details via CRM API.
6๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐ฅ๐ผ๐น๐ฒ & ๐๐ฒ๐ ๐ง๐ฎ๐๐ธ๐
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I donโt offer legal advice.")
๐ Example: A financial AI focuses on finance, not general knowledge.
7๏ธโฃ ๐๐ฎ๐ป๐ฑ๐น๐ถ๐ป๐ด ๐ฅ๐ฎ๐ ๐๐๐ ๐ข๐๐๐ฝ๐๐๐
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution
๐ Example: A financial AI converts extracted data into JSON.
8๏ธโฃ ๐ฆ๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐๐ผ ๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐ฆ๐๐๐๐ฒ๐บ๐ (๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?
๐ Example:
1๏ธโฃ One agent fetches data
2๏ธโฃ Another summarizes
3๏ธโฃ A third generates a report
Master the fundamentals, experiment, and refine and.. now go build something amazing!
1๏ธโฃ ๐๐ต๐ผ๐ผ๐๐ฒ ๐๐ต๐ฒ ๐ฅ๐ถ๐ด๐ต๐ ๐๐๐
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses
๐ Tip: Experiment with models & fine-tune prompts to enhance reasoning.
2๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐๐ผ๐ด๐ถ๐ฐ
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.
๐ Choosing the right approach improves reasoning & reliability.
3๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ผ๐ฟ๐ฒ ๐๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป๐ & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ๐
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?
๐ Clear system prompts shape agent behavior.
4๏ธโฃ ๐๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐ ๐ฎ ๐ ๐ฒ๐บ๐ผ๐ฟ๐ ๐ฆ๐๐ฟ๐ฎ๐๐ฒ๐ด๐
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.
๐ Example: A financial AI recalls risk tolerance from past chats.
5๏ธโฃ ๐๐พ๐๐ถ๐ฝ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐ ๐๐ถ๐๐ต ๐ง๐ผ๐ผ๐น๐ & ๐๐ฃ๐๐
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Description: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?
๐ Example: A support AI retrieves order details via CRM API.
6๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐ฅ๐ผ๐น๐ฒ & ๐๐ฒ๐ ๐ง๐ฎ๐๐ธ๐
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I donโt offer legal advice.")
๐ Example: A financial AI focuses on finance, not general knowledge.
7๏ธโฃ ๐๐ฎ๐ป๐ฑ๐น๐ถ๐ป๐ด ๐ฅ๐ฎ๐ ๐๐๐ ๐ข๐๐๐ฝ๐๐๐
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution
๐ Example: A financial AI converts extracted data into JSON.
8๏ธโฃ ๐ฆ๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐๐ผ ๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐ฆ๐๐๐๐ฒ๐บ๐ (๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?
๐ Example:
1๏ธโฃ One agent fetches data
2๏ธโฃ Another summarizes
3๏ธโฃ A third generates a report
Master the fundamentals, experiment, and refine and.. now go build something amazing!
โค1
Forwarded from Data Science & Machine Learning
๐ฑ ๐ฅ๐ฒ๐ฎ๐น-๐ช๐ผ๐ฟ๐น๐ฑ ๐ง๐ฒ๐ฐ๐ต ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ฒ๐๐๐บ๐ฒ โ ๐ช๐ถ๐๐ต ๐๐๐น๐น ๐ง๐๐๐ผ๐ฟ๐ถ๐ฎ๐น๐!๐
Are you ready to build real-world tech projects that donโt just look good on your resume, but actually teach you practical, job-ready skills?๐งโ๐ป๐
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Are you ready to build real-world tech projects that donโt just look good on your resume, but actually teach you practical, job-ready skills?๐งโ๐ป๐
Hereโs a curated list of 5 high-value development tutorials โ covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learningโจ๏ธ๐ป
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๐ฆ๐๐ถ๐น๐น ๐๐ฎ๐ถ๐น๐ถ๐ป๐ด ๐ง๐ฒ๐ฐ๐ต ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐? ๐ง๐ต๐ฒ๐๐ฒ ๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐๐ผ๐๐น๐ฑ ๐๐ถ๐ป๐ฎ๐น๐น๐ ๐๐ต๐ฎ๐ป๐ด๐ฒ ๐ง๐ต๐ฎ๐๐
Youโve spent hours solving LeetCode problems. Youโve gone through entire DSA playlists๐ฃโจ๏ธ
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โค1