At the end of the day, the time earned is more important then the cost for big players.

The ability to spawn 10 claude agents and rush a project to outcompete someone is more important for big businesses in my imo. Also the small details that GLM missed would take significant more time to iron out, considering it already took double the time.

I do hope other (open weight) models catch up, but to act like they are anywhere close for me is a bit disingenuous.

Part of the core responsibilities is mentoring juniors I would love to learn from people's experiences as much as I can about what works and what to avoid.

Any point of view is welcome, from the junior perspective or the senior perspective, even more grateful if you can give me anecdotes about other senior responsibilities (taking ownership).

I haven't been able to have or find any mentors to guide me on this process, my career has been quite bootstrapped so far, so any advice you can give is beyond welcome.

thanks a lot!

Comments URL: https://news.ycombinator.com/item?id=47764665

Points: 1

# Comments: 1

Overreaching arguments that sellers are like selling because they plan to buy when it's lower, no proof and a limited view, in fact in my also overreached argument I would say the opposite, most people just want to put money on an ETF and hold it until retirement, without having to touch it, they sell because something is forcing their hand and they need the liquidity to pay for something else.

Gold is definitely a hedge for inflation and market instability which is why it's had such a big run up these past few months, and they are definitely used in most diversified portfolios, yale fund as an example, (I don't know where you got this notion from)

My main point is that most people, including the media, whenever there is a big crash in prices, like silver going down double digits, they act like the money evaporated and everyone that invested lost money.

My point is that it's not the case, it dropped because there was a huge volume of people selling, making it cheaper. The people selling converted it all for liquidity, they just 'got' a lot of money in cash to spend, and they needed it or will use it for one reason to another.

Retail investors don't have the time (unless you work in finance) to read all the news and information to be aware of situations that will trigger liquidity crunches like these past few months, while institutional investors will.

My point here is you could have performed all of the value investing in the world and you are still eating losses, standard diversification theory is to put in gold when the markets are unstable, as it appreciates in time of high volatility, we are in times of extreme volatility and gold crashed, it makes no sense unless you have visibility in the institutional investing trends.

For me it was obvious something was afoot with earnings and performance not matching the prices, I finally understand why now thanks to this article.

The fact that there are rules for institutional investors and retail investors and us in retail have so little visibility and time to keep up, just shows more and more the game is a david vs a goliath, and we are all slingless david.

Managing context is by far the most important skill to be effective with LLMS, in addition to having already existing clean code on the codebase.

As they read your files, you are one shot training the LLM in how to write code and how you structure it and it will adapt. With clean codebases, I found the LLMs were outputting well documented, well logged, and even tested functions by default because the other files it interacted with were like this, 'it learns'.

Additionally you have to think how they train and evaluate the model, there are so many use cases to cover, I'm pretty sure in the Reinforcement Learning part they are not going in huge long threads, but are actually benchmarking and optimizing from fresh context starts, and you should do that as much as possible in your tasks.

Their balance sheets will trickle slowly down as dependent countries phase slowly, independently and sensibly. Given they can't see the effects near them (USA's own market is so huge it gives the impression an isolationist arrangement will suffice), I do really believe the rope will thin unnoticed for quite some time until it snaps, and USA Big Tech and the US government will scratch their heads in confusion on what changed and how they got there.

I review every single AI edit with the same cognitive load as if I was programming myself (Claude Code Opus 4.5) and I'm always having to adjust and fix things on a constant basis.

I keep doing it because having the LLM output is basically like a giant auto complete I can tweak, I can't compete with the speed of a proposed patch of me hand writing everything even if I'm considered 'fast' at a 90 WPM and using vim keybindings.

There has never been once a single session or non-trivial task where I would have to NOT intervene in the implementation and I consider myself a quite strong power user, (Master's in AI) using it for a long time, strong linting, and demanding test coverage.

It boggles me and I stand in disbelief with people saying they just let it run by itself and works (fulfilling all edge cases needed for production code NOT the happy path in a PoC) , has not been my experience at all.

I predict the following 3 things:

1.) The people using autonomous agents don't deploy any of the vibe coded mess in a high stakes production environment where bugs and crashes and unintended behaviours will make you lose money and reputation.

2) The people churning 20 agents non stop don't have the skill to realize the slop and mishaps of the code they are pushing.

3) These people have far better prompting skills and stronger setups than me and they can achieve better and more reliable results.

I don't know what it is, probably the third, but it has not matched my reality at all.

My manager is completely non technical with no coding background, but contacts and good knowledge of the business.

In the team of 8 I am the most experienced having 1 year as an intern/junior in another company before this one (I am now 2 years in this job) because I spend a huge amount of time teaching myself, and doing side projects, and enjoy coding outside work.

The company sees AI as R & D outside the core business and committed in hiring people with no experience to not spend much on the salaries of the team.

This has led to the half blind (me) leading the blind (the rest of the team) teaching concepts I've investigated and learned on my own time, and asking AI to continuosly roleplay as a FAANG Staff Engineer and critique my code.

This has led me to start implementing logging, unit tests, integration tests, e2e tests, design patterns like router-service-repository, etc...

We are a pure Python shop mainly composed of data scientists, I tried introducing Go for CLI's and backends met with huge resistance, as well as basic JS (typescript is out of question) for frontends mainly Vibe coded.

I have a lot of freedom on how I implement my own projects (positive of my manager being non technical) and have set precommits on my solo projects were I can't commit without full linting, 80% test coverage, and tests passing (despite these standards not being needed at all).

Given these circumstances, those who don't have the chance of having a mentor, I'm sure I'm not the only one, what's the best way to keep improving so I can be set up the best by the time the job market improves?

Comments URL: https://news.ycombinator.com/item?id=46629361

Points: 1

# Comments: 1

The main problems I see on people not having success with AI are the following:

- Not spending enough time on understanding how to prompt properly, and configure your setup to contextualize the AI properly i.e. Markdown files that: summarize your project structure, explain backend or frontend workflows, business logic, and design decisions, coding standards, (CLAUDE.md for CC users) where you can easily tell the AI to read and they will code how you want.

- Check every single LLM output and patch suggestion with the SAME cognitive load you would use to actually coding it yourself. This is the most important, or else you are comparing apples to oranges.

- Context Engineering: Using subagents to find out how a function or pipeline works end to end to feed your main agent with a succinct summary, keeping the main coding agent on track as multiple diverting tasks poisons the context and effectiveness massively.

- Ask for a sub Agent to verify the work given the spec, with a goal for maintainability, scalability and security.

- Linting (with strict standards), formatting and testing rigorously ( I have them as pre-commits and forbid any commits that have a single linting issue or less than 80% test coverage (if applicable).

Following this I have had massive successes for the simple reason the LLM can write code way faster than I could possibly type. For me this is the main productivity gain of LLMs if you have it set up properly, it can be a massive autocomplete, where if correctly enforced and contextualized it can make huge productivity gains because it can simply write code multitude times faster than I could possibly physically type, inherently making me more productive. This is someone with 90+ WPM using Vim.

Fully agentic autonomous workflows for me are a pipe dream and not feasible at all given due to silly optimizations that backfire, most notably wanting to preserve patch context windows when patching a file, and importing modules (for python) in the middle of the script, or making extremely silly workarounds for a simple syntax error.

If people took the time to set up the proper guardrails, gave it the same cognitive load as normal programming, hopefully they could see the massive boost I have seen, it truly is remarkable especially the more you understand and know the whole codebase because you can easily contextualize what it needs and it produces a solid first draft you just have to edit.

For these reasons, I take it with a massive grain of salt this article.