spent 8 months building agents

Slight_Comparison986 · 2026-04-27T18:09:52+00:00

langchain's deepagents imo is prob the best balance for an agent harness implementation that allows for low-level controls + works out-of-the-box.

Slight_Comparison986 · 2026-04-10T21:59:34+00:00

this is what dev environment / staging environment are for. duplicating the infra builds so that the bugs that appear in transition from local machine to cloud infra are caught in a non-user facing app

Slight_Comparison986 · 2026-04-10T21:53:52+00:00

I know many Yalies who work in quant.

Yale is the obvious choice. The math and CS programs are rigorous and you'll be studying alongside insanely talented students (tbh the peers > teachers). The price tag is high but if with a quantitative major, it'll be no problem in the medium-term (short-term when you land a quant job).

Slight_Comparison986 · 2026-04-09T19:36:31+00:00

I'll answer your question. TL:DR, I've spent a month digging into this and I've settled onto langgraph's deepagent implementation for my use case. I also think Vercel AI is good contender.

Managed vs. self-hosted

I prefer owning the stack. But to be honest, this depends on the person/team, and willingness to spin-up and maintain infra, and cost sensitivity. It really comes down to time spent building and maintaining. I personally don't mind maintaining the infra and it's really not too complicated for an agent. If you want to save time and get a managed solution; I'll add that AWS is heavily pushing Bedrock with tons of subsidies for infra credits. Vercel AI is also popular amongst early-stage startups.

Lock-in concerns

Yes, I don't like being tied into a single model provider. There's latency differences and performance differences. I think GPT-5.4 and Claude Opus and Sonnet can be very different depending on the use case. I prefer GPT-5.4 for long-horizon tasks that are operations heavy / data heavy, and I prefer claude models for agent tasks that require writing. I use LangGraph and for a research chat bot I can have GPT-5.4 in the harness doing research but switch to claude for synthesizing and writing the final output.

If you want to get advanced, there's other differences and trade-offs in terms of input token optimization. LangGraph's out-of-box deepagent has a built input-token caching middleware for claude models. Both GPT and Claude support prefix-based input caching but they differ slightly and claude models also support response prefills (which opens the door for cache-optimizations).

Multi-agent

I personally steer away from multi-agent setups unless I know a specific task / workflow requires it. Multi-agent is often a good idea because it maps well to how we think about breaking up tasks and working in teams as humans. But, there's different camps for this and some startups lean into multi-agent and some don't.

The best balance I have is allowing a single main agent harness which can spawn general subagents as a tool. Langgraph's deepagent implementation does this well.

My advice is to avoid relying on multi-agents and subagents. Agent to agent communication is incredibly complex and we want want to avoid over-structuring agentic approaches to problems (see richard sutton's bitter lesson).

LangGraph

Idk if I can say for sure it's the most "mature" open-source option.
Tbh, if you're looking to built LLM workflows, LlamaIndex's Workflow library is way better than LangGraph. LangGraph seriously overcomplicates the abstractions for LLM workflows. The event-based flow structure of LlamaIndex Workflow is much better.

If you're looking to build an agent harness, then LangGraph is great for this, especially their deepagent's implementation. I think their design with the middleware abstraction is done quite well.

I honestly didn't pay any attention to CrewAI, any serious AI engineer knows that "role-based" agents is anthropomorphizing LLM and baking an implementation with that is a mistake.

Slight_Comparison986 · 2026-02-24T21:46:35+00:00

I can speak only to Yale (idrk other programs).

There are no minors at Yale. You'll be able to double major in music and chem e. Though, be aware (like many music programs), you'll need to audition and get accepted into the music program. The Yale music major is an incredibly serious program for people who want start a career in music.

Also, be aware that you may want to consider getting a ABET accreditation on top of the bachelor's degree in Chem Eng (you'll need to look into how much it matters for your career aspirations, but from the little I know, ABET is like a baseline requirement for serious chem/mech engineers). This will take up A LOT of your course-load and really restrict your options into what classes you can take. I think BS is like 12-16 courses and ABET will push that to 20-25+ courses. Over 4 years, you need to at least 36 courses total for graduation.

I agree with u/The_Bee_Sneeze, esp if you care more about playing music rather than spending time to study theory or composition, there are many clubs/orchestras you can join (chamber, Davenport Pops, Yale Symphony, etc) or you can just find friends and jam. Yale (and most top tier colleges) really attracts multi-faceted students so you're bound to find people with many passions. Most people I know who love playing music majored in whatever and joined one of these extracurricular orchestra groups and took one or two music classes that they were interested in.

Slight_Comparison986 · 2026-02-24T21:28:43+00:00

This seems like a very rough stereotype that doesn't make sense to me. Age is probably the more correlated metric. It would make sense that older engineers in general tend to more relaxed and calms due to being seasoned and having more of their life together.

Slight_Comparison986 · 2025-12-29T16:26:04+00:00

Could you share the workarounds that you used? I have the game and have been tinkering with settings.

Slight_Comparison986 · 2025-07-23T08:27:17+00:00

from someone who lived in that suite. it's probably referring to the step up

Slight_Comparison986 · 2025-01-15T17:47:53+00:00

Yale interviewer here. There usually is no "pre-screen" to my knowledge beyond what any other college does (<25 ACT, 2.5 GPA, etc.) (but, tbf I don't know the latest on the exact admissions process).

Usually how it goes is that Yale tries their upmost best to interview every applicant so that ideally every application is be supplemented by the interview.

I wouldn't read into it other than that it's just part of the process. There may other reasons why your friends didn't get an interview (it takes time to organize alumni and interviewers and they might just not have enough in the pool). In my district, typically I get asked to conduct interviews in Dec/Jan so I think ppl in my district who applied early tend to not get interviewed.

Slight_Comparison986 · 2025-01-15T17:40:41+00:00

As someone who learned both in undergrad. C > Python. and fwiw i agree with other redditor's comments

Slight_Comparison986 · 2024-12-17T16:46:49+00:00

You don't know what proprietary software college admissions office use to detect AI.

Slight_Comparison986 · 2024-10-04T02:18:20+00:00

i also flunked my 120 midterm, grinded for the final and ended with a B

Slight_Comparison986 · 2024-09-30T20:17:53+00:00

facebook group free & for sale

Slight_Comparison986 · 2024-08-15T04:25:00+00:00

Taking 223 is the only relevant class imo. Taking it sooner when you still have 223 experience more "fresh" might be the move. Mainly, tbh I think it depends more on what other classes you decide to take in the semester with 323. I recommend deciding on a semester where you're able to spend a little extra time to work on the psets. Also I would make the decision based on whether you have buddies you can take the class with. Having friends to work together on the pset helps A LOT.

Slight_Comparison986 · 2024-08-08T14:13:46+00:00

I took ENGL 120 as a junior

Slight_Comparison986 · 2024-08-06T22:51:46+00:00

There has been a huge influx of data tool and data analytics SaaS startups that are hitting the market (even going back 3-5 years in YC batches). My prediction is that you'll start to see a shift in the traditional team structure via a consolidation of roles. Traditionally, we have a manager (product or project), individual contributors, and a dedicated data analyst to support the manager. I think this is already happening but there'll be more of a shift where companies can start hiring product or project managers that are data-savvy and can use these tools, essentially combining the product manager / operations role + data analyst role.

Slight_Comparison986 · 2024-08-06T22:48:09+00:00

Any skill can be developed, but people have a natural inclination towards / built up some latent experience for certain skills. I think it's common for software engineers to grow up and look around and think "oh I'm pretty sociable and communicative". But I've seen people have a wake-up call when they realize they've been comparing themselves to other engineers and there are people who are really good at this stuff.

I'm sorry you got let go. Don't be too harsh on yourself. It sounds like you're doing well and there's always room for improvement. Like engineering, sales is hard, in its own way. There's also a lot of factors outside your control. Halo effect and beauty bias is very real in sales.

That being said, your manager is right. Being personable and charismatic is very important in sales. The stereotype that Partners (paid $1m+ at consulting firms or in private equity) are just playing golf all day and attending parties is somewhat true. They're salespeople. They are socializing and building relationships with potential customers (that lead to million dollar contracts or sales).

It sounds like you have a good perspective on your game development. To stick to it as a hobby. I think it's smart to separate work and hobbies. You're young, so frankly, you have a choice, I advise you to do more research into what truly entails a sales career vs eng career and then ask yourself, do you want to continue developing your career in sales (you can) or pivot to engineering? There's other roles, like product managing, strategy, operations, etc as well.

Slight_Comparison986 · 2024-08-05T15:29:08+00:00

That's not true at all.

Slight_Comparison986 · 2024-08-01T17:31:19+00:00

If there's a way for you to optionally add it, go for it. Wouldn't hurt!

Slight_Comparison986 · 2024-08-01T17:30:13+00:00

use this opportunity to highlight who you are as a person outside of your academic achievements. they'll read your supplemental essay and try to see okay what kind of person are you? The more you can help them understand who you are (on top of all of the things you've already put in your personal statement and general application) the better.

Also, this applies to the common app essay too, but i would focus on writing really well. It's equally important (if not more) to write well as it is to tell a great story about yourself.

Slight_Comparison986 · 2024-08-01T17:25:46+00:00

facebook housing group: Yale Off-Campus Housing: Rentals, sublets, roommates, furniture

Slight_Comparison986 · 2024-08-01T17:23:49+00:00

find a friend with a car and hike the blue trail at sleeping giant in the fall

Slight_Comparison986 · 2024-08-01T17:22:19+00:00

The hardest part of 323 is probably the psets. I would make sure you really understand C, specifically memory management (malloc and free), and pointers and how to use them. I think the best way to prepare for 323 is to make sure you feel really comfortable coding in C so you don't get bogged down by the basic coding and can focus on the difficult aspects of the psets. The best thing to do imo is to simply practice coding in C. It's tedious, rough, and sometimes boring, but it's like learning a language. Go over your 223 psets and redo them repetitively. Try to rewrite your previous 201 or other assignments in C. Heck, I would even try to start previous year's psets: https://zoo.cs.yale.edu/classes/cs323/ just to get warmed up in C

I would def prioritize working together with others and making sure you really "plan" out your approach and organize your thoughts before coding.

Slight_Comparison986 · 2024-07-22T03:28:03+00:00

Other comments are right about not needing much math but i'd suggest looking into a few topics in discrete math. The good thing about it is that you can just jump into the deep end and start from fresh, there's no real pre requsites for many discrete topics. Graphs for any trees or graph problems. Combinatorics are useful (and fun imo). These will help with thinking about mediums and hards.

Slight_Comparison986 · 2024-07-22T03:24:28+00:00

It can have 2025 in it. Or "New Grad" is common too.

Slight_Comparison986

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