¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Abeni Codes
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I post about my insights, new discoveries, projects and advices related to tech mainly and other topics once in a while. You can DM me @abeni_al7 for professional or collaborative queries.
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Publicaciones del Canal
| 2 |  Is the interview process for other roles really just convo and stuff? | 104 |
| 3 |  Sin texto... | 154 |
| 4 |  Its fully Confirmed now That Anthropic is adding Ethiopia to Supported Countries List!!!
Its rolling from Claude code first! | 122 |
| 5 | The one thing I understood that made Software Engineering approachable for me was that everything is a tradeoff.
There is no perfect solution to any problem. No solution comes with only upsides. The solution to a problem also introduces another overhead.
Our job is to balance the pros and cons of our chosen approach so that it fits into the requirements of the business we're solving the problem for. | 122 |
| 6 | Using AI for developing Software can indeed speed up delivery if done the right way. I personally like the plan, test, code, refactor loop better when working with AI agents to do this in a safe way. Reviewing the code generated is also a non-negotiable, as code by itself is a liability, and we do not want a lot of code in our codebase for which we have no context for how it works. This is also why I am skeptical of posts where people are claiming to have been generating thousands of lines of code per day.
In contrast, always using AI for coding also wears out our coding skills over time. I fear that this, in turn, might wear out our code reviewing skills as well in the long run. This is why I think we should still be coding by hand from time to time.
Although I myself have been using AI for coding at work for a long time now, I have recently decided to avoid AI while doing projects for my own learning. I think this would help me train my coding muscles and stay sharp in my reviewing skills as well.
I still use AI for writing tests, refining my documentation, and doing small refactors in my personal projects. But I have decided not to write my own projects from scratch with AI. | 120 |
| 7 | Learn AI for free directly from top companies.
1 - Anthropic:
anthropic.skilljar.com
2 - Google:
grow.google/ai
3 - Meta:
ai.meta.com/resources/
4 - NVIDIA:
developer.nvidia.com/cuda
5 - Microsoft:
learn.microsoft.com/en-us/training/
6 - OpenAI:
academy.openai.com
7 - IBM:
skillsbuild.org
8 - AWS:
skillbuilder.aws
9 - DeepLearning.AI:
deeplearning.ai
10 - Hugging Face:
huggingface.co/learn | 98 |
| 8 | I have been using Zenith for a while and I noticed a bug caused by a hard-coded value during development in the analytics. I have since resolved the bug and also added some polishing.
I thought this was only going to be used by me, but I was amazed by the number of people who let me know that they liked it and that they would be using it. I just got a very small number of messages from people who tried it but it still feels good to know that my code is serving others in addition to myself. | 118 |
| 9 | Software is not just about writing some code that runs to perform some tasks and calling it a day.
Software should be:
- Well tested, so that future functionalities are less likely to break the already working parts.
- Well documented, so that it is easier to extend when new functionalities are needed, easier to debug when problems arise and easier to reason about to maintain it for a long time.
- Cleanly structured, so that it is easier to test, document and improve easily. | 162 |
| 10 | I built my own JSON parser in Go and it thought me a few things.
1. Lexical analysis: Going through a string and extracting valid pieces aka tokens. I built a JSON lexer that goes through a string and picks up braces, array brackets, strings with validation for escape characters inside them, numbers, boolean and null keywords. This is the first part for any tool that analyses syntax.
2. Syntactic analysis: Going through the valid tokens and validating their correct placing in the overall structure. I built a parser that recursively parses the tokens in their respective order validating they are placed in the correct order.
3. Recursive Descent: I've only really used recursion when I was solving DSA problems before but I saw a real use case for it when I was building a JSON parser. The logic would have been very bloated and hard to follow without recursion.
4. Edge cases: There are many edge cases when parsing JSON so it allowed me to think about some very minor details which can make or break the functionality. I implemented white space ignoring capability for the lexer, validated the escape sequences for validity, made the number identification robust by handling positive and negative integers, exponential and fractional numbers while catching cases where invalid leading zeros exist. I also implemented a max nesting depth in the parser.
Building such projects is a really amazing learning experience where attention to detail and patience is tested. I'm glad for having done it.
Here is the link for the repo: https://github.com/abeni-al7/cuneiform
Project idea credit: https://codingchallenges.fyi/ | 134 |
| 11 | There is no denying that AI agents has changed the way we do things as developers.
1. Understanding a New codebase
Before the agents: We would go through the documentation (if it exists it has always been very helpful) and the README and try to follow the instructions to setup the project on our machine. We would then go through the architecture diagram to understand the high level architecture and we would go to the specific parts of the codebase where each component is implemented to try and understand how each part is implemented and also to get a feel of how the coding standards are like for that project. We might even trace the flow of data using pen and paper to better understand a specific part where we are performing our tasks.
Now: Although the above methods are still helpful for us we might use an AI agent to summarize the docs, generate an architecture diagram where it does not exist, explain specific components, trace out and display data flow on a particular part. We can even have a conversation with the AI agent asking it questions and describing our assumptions to better understand the codebase in a conversational style. Of course if the codebase is very large, it takes better planning and context feeding to get the desired outcome from the agent.
2. Debugging
Before the agents: When we receieve a bug report, we try to reproduce it on our machine. If we can't it is even harder to debug since there might be deadlocks or race condition issues that presented themselves in a production environment while being invisible for us. So we might resort to utilizing our observability metrics and using stress testing to figure out the problem. We then try to trace the flow of data through the code using print statements or a debugger and identify where it went wrong. We would then plan and implement a fix based on our understanding of the issue.
Now: We can utilize an AI agent in every step of finding out where the issue lies, figuring out what went wrong and planning and implementing a fix. We do need to control how much we are outsourcing to the AI agent so that we can be sure about the result and we do not lose context of what we are doing.
3. Writing tests & docs
This is by far the best utility of AI agents to save our time on writing verbose tests and detailed documentations. We can actually ask the AI agent to write tests and we can read the test and look for uncovered edge cases which we can iterate with the AI agent to fill out. We can also let the AI agent write docs and we can read, iterate and verify with improved efficiency compared to what we would have done before AI agents.
4. Implementing new features
When we plan a new feature, we can involve an AI agent to poke holes in our plans, remind us of our oversight and even give us new alternatives to explore and decide on. We then can feed the AI agent our finalized plan and let it write the code for us. We then need to review the code and validate everything is according to plan. Which improves our efficiency a lot by focusing our attention on the plan and architecture instead of also typing out every single line of code. This works better if we provide the agent with clear instructions and an unambiguous plan to the best of our abilities.
We still need our judgement and technical skills to produce quality work. We can also utilize AI to accelerate our progress while doing our work. | 116 |
