标签：#programming

submitted by /u/Efficient-Public-551 [link] [留言]

There's a moment in every ambitious AI engineering project where you convince yourself that more agents means more power. I hit that moment early in building my Python orchestration framework — and I spent several painful weeks learning exactly why that intuition is wrong. The seductive pitch: decompose complex tasks into specialized sub-agents, run them in parallel, let them coordinate. What actually happened was a reliability nightmare that taught me more about agentic architecture than any framework documentation ever could. The Problem I Actually Built My Python orchestration system was designed to automate complex, multi-step workflows — the kind that require planning, research, code generation, and validation to happen in a coherent sequence. Early on, I structured it as a web of parallel agents: a planner, several workers, a validator, and a synthesizer, all exchanging structured messages. On paper it was elegant. In practice, it had three failure modes I couldn't engineer away: Context drift. Each agent only saw the slice of information it was handed. The worker writing one module couldn't see what the worker writing another module had decided. By the time the synthesizer tried to combine outputs, I had conflicting assumptions baked into the results — variable names that clashed, patterns that contradicted each other, interfaces that didn't align. Cascading partial failures. When one agent produced ambiguous output, every downstream agent amplified the ambiguity. A planner that returned a slightly underspecified task description produced workers that each interpreted it differently. Nothing failed loudly. Everything just drifted, quietly, until the final output was incoherent. Debugging opacity. When something went wrong in a parallel multi-agent system, tracing the failure was miserable. Was it the planner? One of the workers? The message-passing layer? I'd rebuilt the worst parts of distributed systems debugging inside a single Python process. The Architec

submitted by /u/Dear-Economics-315 [link] [留言]

submitted by /u/lelanthran [link] [留言]

Beyond the Prototype: Why Teams Need More Than Vibe Coding Over the last year, AI coding tools such as Lovable, Bolt.new, v0, Base44, and others have fundamentally changed how software gets created. A single founder or developer can now go from a rough idea to a working prototype in hours rather than weeks. That kind of acceleration is genuinely exciting, and it has opened software creation to far more people. That democratization is a good thing. Rapid experimentation, faster feedback loops, and lower barriers to entry are changing how products get started. Many successful companies and ideas will emerge because these tools made building more accessible. As I've followed the conversations happening around these tools—through reviews, articles, community discussions, and the experiences being shared by founders and engineering leaders—I've noticed an interesting pattern. The challenge is no longer getting to the first version. The challenge begins after. The Prototype Was Never the Finish Line The prototype works. Stakeholders become excited. Customers show interest. Momentum builds. Then a different set of questions starts to emerge. How do we align everyone on what we're building? How do we evolve an existing application instead of starting over? How do we maintain quality as complexity increases? How do multiple people collaborate without losing context? How do we know whether we're delivering the outcomes we intended? And how do we continuously improve without creating chaos? These aren't failures of AI coding tools. They're simply different problems. Many of today's AI builders are optimized for individual acceleration and rapid exploration. But once a promising idea becomes a product that teams must own, maintain, and evolve together, different requirements naturally emerge. What works for one person experimenting is not always enough for a group of people building something intended to last. Building Software Is More Than Generating Code Software development

Disclosure: This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article. Hello friends, System design ** and ** Software design are two important topics for tech interviews and two important skills for Software developers. Without knowing how to design a system, you cannot create new software, and it will also be difficult to learn and understand existing software and systems. That's why big tech companies like FAANG/MAANG pay special attention to System design skills and test candidates thoroughly. Earlier, I have shared system design interview questions like API Gateway vs Load Balancer , Horizontal vs Vertical Scaling , Forward proxy vs reverse proxy , and common System design concepts , and in this article, I am going to share with you the best System design books to learn Software design. Whether you are a beginner or an experienced developer, you can read these books, as you will definitely find valuable stuff. I have read them, and even though I have been doing Software development for more than 15 years, I have learned a lot. System design ** is a complex process, and you need to know a lot of stuff to actually design a system that can withstand the test of time in production. Software architecture is another field where you are expected to learn a lot of things. It's simply impossible to become a software architect by reading a few books, but if you have experience and a hunger to learn, then these books can be a gold mine. These books allow you to learn from other people's experiences. You can read these books to find what challenges they face when they design a real-world system like Spotify, Google, or Amazon, and how they overcome. Each story is a journey in itself, and you will learn a thing or two by reading and then relating with your own experience. I love to read books, and they are my primary source of learning, along with online courses nowadays. In this art

submitted by /u/elemenity [link] [留言]

submitted by /u/goto-con [link] [留言]

submitted by /u/compilersarefun [link] [留言]

submitted by /u/macrohard_certified [link] [留言]

submitted by /u/Either_Collection349 [link] [留言]

submitted by /u/carlievanilla [link] [留言]

submitted by /u/CircumspectCapybara [link] [留言]

submitted by /u/grouvi [link] [留言]

submitted by /u/alexeyr [link] [留言]

What Are the 36 Stratagems? If you've heard of The Art of War, think of the Thirty-Six...

Many Python beginners can write basic programs but get stuck when they try to run a real project on their own laptop. The issue is not always coding. Sometimes the real problem is setup . You may know loops, functions, and lists, but still face problems like: ModuleNotFoundError Python is not recognized Package installed but not working in VS Code Wrong interpreter selected These are common beginner setup issues. Why online compilers are not enough Online compilers are good for quick practice. But real Python projects need: project folders multiple files external packages virtual environments dependency files terminal commands debugging tools Git basics So, when the goal is to build real projects, it is better to move to a local Python setup early. Basic Python project setup flow A simple Python project setup flow looks like this: Install Python Install VS Code Create project folder Create virtual environment Activate virtual environment Install packages Save requirements.txt This setup may look basic, but it prevents many beginner-level errors later. Example folder structure A beginner-friendly Python project folder can look like this: python-project/ │ ├── main.py ├── requirements.txt ├── README.md └── venv/ Here is what each file or folder means: main.py is the main Python file. requirements.txt stores project dependencies. README.md explains the project. venv/ contains the virtual environment. Create a virtual environment Create a virtual environment using: python -m venv venv Activate it on Windows: venv \S cripts \a ctivate Activate it on Mac/Linux: source venv/bin/activate A virtual environment keeps each project’s packages separate. This helps avoid package conflicts when working on multiple Python projects. Install a package After activating the virtual environment, install packages using pip . Example: pip install requests Now create a Python file: import requests response = requests . get ( " https://api.github.com " ) print ( response . status_code ) If

submitted by /u/j1897OS [link] [留言]

submitted by /u/Either_Collection349 [link] [留言]

submitted by /u/Inevitable_Back3319 [link] [留言]