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VGG19 Transfer Learning Explained for Beginners
https://www.reddit.com/r/programming/comments/1p6lhiz/vgg19_transfer_learning_explained_for_beginners/

<!-- SC_OFF -->For anyone studying transfer learning and VGG19 for image classification, this tutorial walks through a complete example using an aircraft images dataset. It explains why VGG19 is a suitable backbone for this task, how to adapt the final layers for a new set of aircraft classes, and demonstrates the full training and evaluation process step by step. written explanation with code: https://eranfeit.net/vgg19-transfer-learning-explained-for-beginners/ video explanation: https://youtu.be/exaEeDfbFuI?si=C0o88kE-UvtLEhBn This material is for educational purposes only, and thoughtful, constructive feedback is welcome. <!-- SC_ON --> submitted by /u/Feitgemel (https://www.reddit.com/user/Feitgemel)
[link] (https://eranfeit.net/vgg19-transfer-learning-explained-for-beginners/) [comments] (https://www.reddit.com/r/programming/comments/1p6lhiz/vgg19_transfer_learning_explained_for_beginners/)
Chimera - an innovative (?) db interface
https://www.reddit.com/r/programming/comments/1p6ln5j/chimera_an_innovative_db_interface/

<!-- SC_OFF -->Today I’m sharing the first architectural preview of Chimera, a project I’ve been building in my free time to both explore system architecture and sharpen my modern C++ skills. Chimera is designed to simplify interaction with heterogeneous databases by offering a single, consistent interface for PostgreSQL, MongoDB, and Oracle. It currently provides: Autogenerated DAO classes from existing DB schemas A speculative in-memory model for each table/collection Multiple synchronization modes (sync, async, real-time) It is aimed to be used in two ways: - Embedded Mode (linked directly into a CSCI) - Hosted Server Mode (exposed via REST API) The goal is to reduce boilerplate, standardize data access, and make multi-DB environments easier to manage — especially in complex, high-reliability systems. I’m sharing the first architecture diagram below ⬇️ and I’d genuinely appreciate feedback, ideas, or constructive criticism. Your insights will help me guide its next steps while continuing to grow as a C++ engineer. Thanks in advance! Ps: At the moment the name is chimera for the Three Adapters, I shall find another mythical animal if I decide to add another one 😂 <!-- SC_ON --> submitted by /u/Altruistic_Pizza_766 (https://www.reddit.com/user/Altruistic_Pizza_766)
[link] (https://www.linkedin.com/posts/marco-pecorella-20b005183_cplusplus-softwarearchitecture-backend-activity-7399115638218240000-vTrn?utm_source=share&utm_medium=member_ios&rcm=ACoAACs_0usBleIFhb1GQ4bxucSy5eHFA94T4aY) [comments] (https://www.reddit.com/r/programming/comments/1p6ln5j/chimera_an_innovative_db_interface/)
Simulating Unix v7 on PDP-11 with In-Depth Explanations
https://www.reddit.com/r/programming/comments/1p6ujl6/simulating_unix_v7_on_pdp11_with_indepth/

<!-- SC_OFF -->I hope I did a good job uncovering the technical details and the relevant history. <!-- SC_ON --> submitted by /u/baziotis (https://www.reddit.com/user/baziotis)
[link] (https://sbaziotis.com/uncat/simulating-pdp-11-with-in-depth-explanations.html) [comments] (https://www.reddit.com/r/programming/comments/1p6ujl6/simulating_unix_v7_on_pdp11_with_indepth/)
Reading Code Is a Useful (but sometimes overlooked) Skill
https://www.reddit.com/r/programming/comments/1p6w0ya/reading_code_is_a_useful_but_sometimes_overlooked/

<!-- SC_OFF -->This video goes into why becoming better at reading code can lead to becoming a better programmer overall and why it might be a skill that can be easy to overlook. <!-- SC_ON --> submitted by /u/stumblingtowards (https://www.reddit.com/user/stumblingtowards)
[link] (https://youtu.be/Apf9OuU7jdQ) [comments] (https://www.reddit.com/r/programming/comments/1p6w0ya/reading_code_is_a_useful_but_sometimes_overlooked/)
Agentic AI: The Hidden Engineering Traps in Building Autonomous Workflows
https://www.reddit.com/r/programming/comments/1p70pol/agentic_ai_the_hidden_engineering_traps_in/

<!-- SC_OFF -->Been building production agentic AI (agents that execute, not just chat) for the last year. Most demos crash in the real world because: RAG chunking/embedding choices destroy retrieval quality (we ended up hybrid Pinecone + FAISS + strict thresholds) ReAct loops run away and burn 10× tokens (memory caps + probabilistic exits fixed it) No real error handling for flaky APIs or garbage input (custom backoff + fallback LLMs) Governance logging without killing speed (event sourcing + replayable traces) End result: 60% less human touch, but only after sweating these details. What’s the nastiest production surprise you’ve hit with agentic builds? <!-- SC_ON --> submitted by /u/Futurismtechnologies (https://www.reddit.com/user/Futurismtechnologies)
[link] (https://www.futurismai.com/solutions/futurism-agentic-ai-solutions/?utm_source=reddit&utm_medium=social) [comments] (https://www.reddit.com/r/programming/comments/1p70pol/agentic_ai_the_hidden_engineering_traps_in/)
Where do you draw the line between Python and a faster language?
https://www.reddit.com/r/programming/comments/1p730ev/where_do_you_draw_the_line_between_python_and_a/

<!-- SC_OFF -->I love Python for speed of development, readability, and libraries but I sometimes feel guilty when performance becomes a bottleneck. Curious how others decide when it’s time to switch to C++/Rust/Go instead of optimizing Python. Do you usually push Python as far as possible first, or do you switch early when performance matters? <!-- SC_ON --> submitted by /u/Creepy-Valuable-3685 (https://www.reddit.com/user/Creepy-Valuable-3685)
[link] (https://benchmarksgame-team.pages.debian.net/benchmarksgame/) [comments] (https://www.reddit.com/r/programming/comments/1p730ev/where_do_you_draw_the_line_between_python_and_a/)