Reddit Programming

Remote Code Execution With Buffer Overflow In C: Stack-frames, Return Addresses and Modern Defenses
https://www.reddit.com/r/programming/comments/1mnfutx/remote_code_execution_with_buffer_overflow_in_c/

When people said 'buffer overflows can be used to execute arbitrary code' in blogs/videos, I wondered how that is possible as for me, a buffer was just a reserved array of bytes in the program's memory meant for 'storage' and not 'execution'. On diving deeper, I was fascinated how return addresses are modified to execute code stored in the buffer and also the security measures undertaken by operating systems and compilers to avoid such attacks. I am not a cybersecurity expert (I'm into ML and Android dev), but the breadth of low-level concepts covered while researching the topic, motivated me to combine all my findings/ideas in a blogpost. The blogpost also describes the process of developing a payload that when given to a vulnerable program can cause a RCE. Do share your thoughts on the topic and the blog!  submitted by /u/shubham0204_dev (https://www.reddit.com/user/shubham0204_dev)
[link] (https://shubham0204.github.io/blogpost/programming/rce-with-buffer-overflow) [comments] (https://www.reddit.com/r/programming/comments/1mnfutx/remote_code_execution_with_buffer_overflow_in_c/)

5 views18:39

Reddit Programming

Designing Software in the Large
https://www.reddit.com/r/programming/comments/1mnpqru/designing_software_in_the_large/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://dafoster.net/articles/2025/07/22/designing-software-in-the-large/) [comments] (https://www.reddit.com/r/programming/comments/1mnpqru/designing_software_in_the_large/)

4 views22:47

Reddit Programming

Takeaway: a work-stealing task queue library for Rust
https://www.reddit.com/r/programming/comments/1mnprx3/takeaway_a_workstealing_task_queue_library_for/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://bal-e.org/speed/krabby/takeaway/) [comments] (https://www.reddit.com/r/programming/comments/1mnprx3/takeaway_a_workstealing_task_queue_library_for/)

3 views22:48

Reddit Programming

A Tour of Standard ML
https://www.reddit.com/r/programming/comments/1mnpryi/a_tour_of_standard_ml/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://saityi.github.io/sml-tour/tour/welcome) [comments] (https://www.reddit.com/r/programming/comments/1mnpryi/a_tour_of_standard_ml/)

2 views22:49

Reddit Programming

Compiling a Lisp: Closure conversion
https://www.reddit.com/r/programming/comments/1mnpsi4/compiling_a_lisp_closure_conversion/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://bernsteinbear.com/blog/compiling-a-lisp-12/) [comments] (https://www.reddit.com/r/programming/comments/1mnpsi4/compiling_a_lisp_closure_conversion/)

2 views22:50

Reddit Programming

Don’t Forget To Flush by Andrew Kelley
https://www.reddit.com/r/programming/comments/1mnpstp/dont_forget_to_flush_by_andrew_kelley/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://www.youtube.com/watch?v=f30PceqQWko) [comments] (https://www.reddit.com/r/programming/comments/1mnpstp/dont_forget_to_flush_by_andrew_kelley/)

2 views22:51

Reddit Programming

Going faster than memcpy
https://www.reddit.com/r/programming/comments/1mnptmo/going_faster_than_memcpy/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://squadrick.dev/journal/going-faster-than-memcpy) [comments] (https://www.reddit.com/r/programming/comments/1mnptmo/going_faster_than_memcpy/)

2 views22:52

Reddit Programming

Operation Costs in CPU Clock Cycles
https://www.reddit.com/r/programming/comments/1mnpu87/operation_costs_in_cpu_clock_cycles/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (http://ithare.com/infographics-operation-costs-in-cpu-clock-cycles/) [comments] (https://www.reddit.com/r/programming/comments/1mnpu87/operation_costs_in_cpu_clock_cycles/)

2 views22:53

Reddit Programming

Generic Containers in C: Safe Division Using Maybe
https://www.reddit.com/r/programming/comments/1mnpua3/generic_containers_in_c_safe_division_using_maybe/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://uecker.codeberg.page/2025-08-10.html) [comments] (https://www.reddit.com/r/programming/comments/1mnpua3/generic_containers_in_c_safe_division_using_maybe/)

3 views22:54

Reddit Programming

Gleam’s Interoperability with Erlang and Elixir
https://www.reddit.com/r/programming/comments/1mnpva7/gleams_interoperability_with_erlang_and_elixir/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://www.youtube.com/watch?v=63Z2oNW1Bf4) [comments] (https://www.reddit.com/r/programming/comments/1mnpva7/gleams_interoperability_with_erlang_and_elixir/)

3 views22:55

Reddit Programming

Rust Type-Level Fuckery
https://www.reddit.com/r/programming/comments/1mnpvbp/rust_typelevel_fuckery/

submitted by /u/ketralnis (https://www.reddit.com/user/ketralnis)
[link] (https://github.com/lilyyy411/rust-type-fuckery/blob/main/README.md) [comments] (https://www.reddit.com/r/programming/comments/1mnpvbp/rust_typelevel_fuckery/)

2 views22:56

Reddit Programming

Hands On System Design with "Distributed Systems Implementation - 254-Lesson’s curriculum"
https://www.reddit.com/r/programming/comments/1mnv4y4/hands_on_system_design_with_distributed_systems/

Check here for detailed curriculum. Why This Course? Build a complete, production-ready system design from scratch in just one year. Each day features practical, hands-on tasks with concrete outputs that incrementally develop your expertise in scalable architectures, component design, and modern DevOps practices. What You'll Build A comprehensive system capable of: Supporting millions of concurrent users Scaling horizontally across distributed infrastructure Processing data efficiently with optimized algorithms Providing responsive interfaces with millisecond latency Supporting multi-tenancy for enterprise deployments Operating with high availability across multiple regions Who Should Take This Course? This course is perfect for: Recent CS Graduates seeking to bridge the gap between academic theory and production-ready skills Job Seekers looking to enhance their resume with demonstrated practical experience Software Engineers wanting to level up from application development to system architecture System Architects interested in modern, cloud-native architectures DevOps Engineers expanding their knowledge of scalable systems Backend Engineers building expertise in high-performance systems Engineering Managers who need technical depth to lead system design efforts Product Managers seeking technical understanding of scalable architectures What Makes This Course Different? Practical Focus: Build real components with tangible outputs every single day Progressive Learning: Start with basics and advance to complex system design concepts Full-Stack Coverage: Spans from low-level optimization to high-level architecture Production Mindset: Addresses security, scalability, observability, and fault tolerance Modern Technologies: Incorporates industry-standard tools like Kubernetes, Redis, and message queues End-to-End System: Complete the journey from individual components to an integrated platform Key Topics Covered Check here for detailed - 254 Lesson course Curriculum (https://sdcourse.substack.com/p/hands-on-distributed-systems-with) System decomposition and service-oriented architectures Scalable backend design with proper separation of concerns Database selection, optimization and access patterns API design and protocol considerations Caching strategies at multiple system layers Load balancing and traffic management Security principles and implementation Performance optimization techniques Monitoring, alerting and observability Join us on this year-long journey to master system design by building a production-grade platform that showcases your skills and opens doors to advanced engineering roles. Start building your system design expertise today.  submitted by /u/Vast_Limit_247 (https://www.reddit.com/user/Vast_Limit_247)
[link] (https://sdcourse.substack.com/p/hands-on-distributed-systems-with) [comments] (https://www.reddit.com/r/programming/comments/1mnv4y4/hands_on_system_design_with_distributed_systems/)

6 views04:26

Reddit Programming

Built a "predictive database" that returns ML predictions via SQL-like queries - GitHub demo with live examples
https://www.reddit.com/r/programming/comments/1mo0u09/built_a_predictive_database_that_returns_ml/

I've been working on something I call a "predictive database" - essentially a system that lets you query for ML predictions using SQL-like syntax instead of building complex training pipelines. The concept: Instead of: Extract data → Train model → Deploy → Monitor drift → Retrain You get: Write query → Get prediction → Done Example query: { "from": "invoices", "where": { "Description": "AWS Cloud" }, "predict": "Processor", "select": ["$p", "Name", "Role", { "$why": { "highlight": true } }] } What makes this interesting: - Works with your existing data immediately (no training phase) - Familiar SQL-like interface for developers - Real-time predictions that improve automatically - Built specifically for workflows like invoice processing, categorization, anomaly detection Try it yourself: - GitHub repo: github.com/aitoai/aito-demo - Live interactive demo: aito.ai/demo - Technical docs: [link to docs] The demo shows real personalization, automation and analytics examples, but the approach works for any prediction task where you need results immediately rather than waiting for model training cycles. Built this based on my long experience in consulting and realization, that lot of data science work could be implemented with queries, if the database is optimized for inference. Technical implementation details: - Ad hoc inference, that builds statistical models directly from inference-optimized database for each query - Automatic feature engineering from relational data
- REST API with SQL-like query language - Handles missing data and uncertainty quantification Happy to answer technical questions about the implementation approach or discuss trade-offs vs traditional ML workflows. What do you think? Is this approach useful for the kind of problems you work on?  submitted by /u/arauhala (https://www.reddit.com/user/arauhala)
[link] (https://github.com/AitoDotAI/aito-demo/) [comments] (https://www.reddit.com/r/programming/comments/1mo0u09/built_a_predictive_database_that_returns_ml/)

3 views06:45

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