If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/daRwW5JFjhAvi Chawla (@_avichawla):Speed up your native Python code by over 50x!And it takes just 4 simple steps.Python’s default interpreter (CPython) is slow primarily because of its dynamicity.For instance, after defining a variable of a specific type, it can be changed to some other type.But these https://t.co/QtcuC4C8rL ...

Starting Python 3.14, another way to speed up Python code is by disabling GIL.Earlier, despite writing multi-threaded code, Python could only run one thread at a time. But now, Python can run it in a multi-threaded fashion.👉 What are some other ways to speed up Python code? https://t.co/DEIOEejKa8 ...

Speed up your native Python code by over 50x!And it takes just 4 simple steps.Python’s default interpreter (CPython) is slow primarily because of its dynamicity.For instance, after defining a variable of a specific type, it can be changed to some other type.But these dynamic manipulations come at the cost of run-time and memory overheads.The Cython module converts your Python code into C.Steps to use the Cython module (refer to the image as you read):1) Load the Cython module: %load_ext Cython2) Add the Cyt ...

Market Trend & Disruption - The cybersecurity industry is facing a potential shift with AI agents replicating traditional penetration testing services [1] - AI-driven pentesting tools can perform routine security checks (pre-launch, post-refactor, quarterly checks) [3] Cost & Time Efficiency - Traditional penetration testing engagements typically cost between $20 thousand and $50 thousand [4] - Traditional penetration testing engagements can take 4 to 6 weeks for scoping, NDAs, and kickoff calls [4] - AI pentesting agents can deliver results in hours instead of weeks, at a fraction of the cost [1] AI Pentesting Capabilities - AI agents can enumerate attack surfaces, fuzz endpoints, and chain simple vulnerabilities [4] - AI agents can produce Proof of Concepts (PoCs) and remediation steps for developers [4] - Open-source frameworks like Strix (with 14 thousand+ stars) are implementing AI pentesting agents [1] Strix Framework Details - Strix creates a team of AI "attackers" to probe web apps, APIs, and code [2] - Strix provides validated findings with exploit evidence, remediation steps, and a full PDF report [2]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/WuwZDFcbOOAvi Chawla (@_avichawla):Pentesting firms don't want you to see this.An open-source AI agent just replicated their $50k service.A "normal" pentest today looks like this:- $20k-$50k per engagement- 4-6 weeks of scoping, NDAs, kickoff calls- A big PDF that's outdated the moment you ship a new https://t.co/l2GGqvyTpa ...

Project Information - Strix GitHub repository is available [1] - Encourages users to star the Strix GitHub repository [1]

Market Trend & Disruption - AI pentesting agents are starting to perform on-par with human pentesters in routine tasks [1] - The routine pentest (pre-launch, post-refactor, quarterly checks) is shifting to AI [3] Cost & Time Efficiency - Traditional pentesting engagements cost $20 thousand to $50 thousand and take 4-6 weeks [4] - AI pentesting can be done in hours instead of weeks and at a fraction of the cost [1] Tool & Implementation - Strix, an open-source AI pentesting framework, replicates a $50 thousand service [1] - Strix spins up a team of AI "attackers" to probe web apps, APIs, and code [2] - Strix returns validated findings with exploit evidence, remediation steps, and a full PDF report [2]

RT Avi Chawla (@_avichawla)Stanford researchers built a new prompting technique!By adding ~20 words to a prompt, it:- boosts LLM's creativity by 1.6-2x- raises human-rated diversity by 25.7%- beats fine-tuned model without any retraining- restores 66.8% of LLM's lost creativity after alignmentPost-training alignment methods, such as RLHF, are designed to make LLMs helpful and safe.However, these methods unintentionally cause a significant drop in output diversity (called mode collapse).When an LLM collapses ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/zWuVT7jV6hAvi Chawla (@_avichawla):Stanford researchers built a new prompting technique!By adding ~20 words to a prompt, it:- boosts LLM's creativity by 1.6-2x- raises human-rated diversity by 25.7%- beats fine-tuned model without any retraining- restores 66.8% of LLM's lost creativity after alignment https://t.co/AOzUKPpnLQ ...

Paper: https://t.co/C95c7b2Pka ...