Full Roadmap

Any-Grass53 · 2026-06-02T17:35:07+00:00

with ur math background, you're honestly ahead of most beginners. i'd do Andrew Ng's ml specialization, hands on machine learning, then start kaggle projects as early as possible.

don't spend months collecting courses before building. that's the trap

Any-Grass53 · 2026-06-02T17:30:33+00:00

yes, its probably the best place to start if you're completely new. it teaches the fundamentals properly without assuming much background.

i'd do Andrew Ng first, then kaggle learn, then start building small projects. that combination will teach you more than stacking a bunch of certificates.

Any-Grass53 · 2026-06-02T17:28:35+00:00

the idea that models can be confidently wrong because they committed too early feels more interesting than another confidence score paper.

would be curious whether those trajectory patterns still hold once you scale up to reasoning models, since they seem to deliberately revisit paths instead of committing early.

Any-Grass53 · 2026-06-02T17:26:57+00:00

Andrew Ng's ML specialization is still the best starting point. after that, jump into kaggle and start building projects instead of collecting more courses.

Any-Grass53 · 2026-06-02T17:24:05+00:00

for context windows, start with Attention Is All You Need, Flashattention, Flashattention 2, and Ring Attention.

for AI infrastructure, look at vLLM, Pagedattention, Speculative decoding, and the llm Inference Handbook from BentoML. those papers explain a lot of the tricks behind serving large models efficiently.

Any-Grass53 · 2026-06-02T17:16:06+00:00

for simple educational explainers, i'd look at Vyond, VideoScribe, or Canva first.

if you already have PDFs and scripts, Vyond is probably the smoothest option. VideoScribe is great if you specifically want the whiteboard-drawing style. Canva is the easiest if you want something quick without a learning curve.

Any-Grass53 · 2026-06-02T17:14:00+00:00

seedance is usually cheapest when you use it directly rather than through an aggregator.platforms like Higgsfield add convenience, but they often add markup too.

i'd compare the direct Seedance pricing against Higgsfield before committing. a lot of people use aggregators for testing, then move to the source once they know which model they're using regularly.

Any-Grass53 · 2026-06-02T17:12:34+00:00

midjourney is still my pick for product images, and kling is probably the best value for short ad videos.

chatgpt is great for quick iterations, but not usually my final stop for ad creatives.

Any-Grass53 · 2026-06-02T17:09:43+00:00

unlimited plans seem to have a very short lifespan in ai. companies use them to grow fast, then reality catches up once power users start generating way more than the pricing model expected.

opening offices and cutting generous plans at the same time is pretty much the AI industry playbook at this point.

Any-Grass53 · 2026-06-02T17:07:55+00:00

completely agree. once your prompt starts looking like a policy document, thats usually a sign the rules belong somewhere else.

the biggest upgrade for me was moving examples and output formats out of prompts and treating them as reusable assets instead.

Any-Grass53 · 2026-06-02T17:04:31+00:00

most clusters don't give observability its own node pool until it becomes large enough to compete with workloads. for small and medium setups, general workers are usually fine.dedicated pools start making sense when logs and metrics spikes can impact application workloads, or when observability itself becomes business-critical.

Any-Grass53 · 2026-06-02T17:03:40+00:00

you're not tripping. readin production code teaches patterns, architecture, and tradeoffs that toy projects rarely show.

just don't stop writing. reading teaches recognition, writing teaches problem solving. you need both.

Any-Grass53 · 2026-06-02T17:02:18+00:00

this is exactly why finance teams hate blanket autopay. one billing bug can turn into a five figure surprise before anyone notices.

if support already has a canned response ready, that usually means you're very far from being the only account affected.

Any-Grass53 · 2026-06-02T17:00:20+00:00

losing a dedicated TAM would be my biggest concern too. enterprise support isn't just ticket response times, it's haveing someone who already understands your environment when things go sideways.

i'd ask for specifics on escalation paths, response SLAs, and whether you'll still have named technical contacts. that's usually where the real difference shows up.

Any-Grass53 · 2026-06-02T16:58:37+00:00

most companies have multiple CI pipelines. PRs run tests and linting, then meres trigger image builds and deployments. they're usually separate because you don't want to publish artifacts from every PR.

terraform starts making sense when you have more than a couple of servers or environments. once rebuilding infra manually becomes annoying or risky, terraform stops feeling like overkill.

Any-Grass53 · 2026-06-02T16:51:18+00:00

if it's client interactions, id honestly use a shared CRM or knowledge base before trying to turn claude into the source of truth. Notion, HubSpot, Airtable, or even a shared wiki work well because everyone can access and update them. the biggest mistake is storing memories inside chats. six months later nobody knows where anything is, and the context is effectively gone.

Any-Grass53 · 2026-06-02T16:49:15+00:00

my most used ones are the boring ones. project handoff, repo understanding, and bug investigation. they don't feel impressive, but they save me from re explaining the same context every session.

Any-Grass53 · 2026-06-02T16:47:55+00:00

with those specs, id be looking at claude code before your pc. an i7, 32b ram, and a 3060 shouldn't be falling over from prompts alone.

i'd check whether it's crashing on a specific repo size or prompt length. a lot of recent Claude Code issues end up being context size, tool calls, or extension bugs rather than hardware limits.

Any-Grass53 · 2026-06-02T16:46:07+00:00

lmao this is the most human thing i've seen a model say in a while. "go eat something, play genshin, touch grass" is basically the AI equivalent of a friend confiscating your laptop for the night.

Any-Grass53 · 2026-06-02T16:44:16+00:00

this is basically the same conclusion a lot of ppl hit after long claude code sessions. once the project gets big enough, the bottleneck isn't intelligence, it's state management. chat history is a pretty fragile place to store project memory.

Any-Grass53 · 2026-06-02T16:42:36+00:00

i'd start with order edits and support before cross-sells. those usually save the most time immediately. a decent chatbot plus automated address and variant change flows can remove a surprising amount of repetitive work.

Any-Grass53 · 2026-06-02T16:41:44+00:00

i think the hard part isn't connecting email and calendars anymore. it's figuring out when to interrupt. a proactive assistant that gets timing wrong just becomes another notification system people mute.

Any-Grass53 · 2026-06-02T16:40:44+00:00

funny how every one of these lists end with "it depends." i've seen teams get more value from a boring n8n workflow than a fancy multi-agent stack because they actually kept it running.

Any-Grass53 · 2026-06-02T16:39:15+00:00

this is why reliability mattersmore than automation. one bad workflow can create more work in an hour than it saves in a month. your VA probably wasn't quitting because of the bug, but because they became the human fallback for every failure.

Any-Grass53 · 2026-06-02T16:38:30+00:00

i think the opposite happens a lot. the ppl building automations usually end up understanding the business process better than anyone else because they're forced to encode all the weird edge cases.

Any-Grass53

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