Jianghong94

5 ms·

1.
by Jianghong94 3mo ago
lolol. Actually, I find AI has a reasonable chance to figure it out, as long as you point to the right source code. BTW to me these quirks actually can be used as some kind of job security. If it takes a year to onboard someone to do meani
2.
by Jianghong94 3mo ago
What I imply from the description is that, the default ring contains some shared global public data (e.g. a cache of bloomberg informations), and each individual team will have their own rings. Afterall there's no that many you can fit
3.
by Jianghong94 3mo ago
Well I doubt these solutions are very useful outside; from what I read what they have is a universal data store (that isn't hard to implement using current off-the-shelf OSS), something for financial instruments that has a compositiona
4.
by Jianghong94 4mo ago
I just went through a similar discussion in my $WORK (traditional finance company on NYSE with average IT expertise) and I think the thought process is as such: it's one thing to just give your stellar dev/hacker a beefy GPU serve
5.
by Jianghong94 4mo ago
Seriously, it's much easier to review the AI generated plan, instead of reviewing their code. What I found is that, if the change surface is small enough, AI can get it right under the correct assumptions, but the noob mistake is to le
6.
by Jianghong94 4mo ago
You know the funny thing is that, the lazy engineer can very likely ask you to just scrap all his code and vibe code again.
7.
by Jianghong94 7mo ago
Honestly I don't understand why they/any fast-and-error-prone model position themselves as coding agents; my experience tells me that I'd much rather working with a slow-but-correct model and let it run longer session than ha
8.
by Jianghong94 8mo ago
This. At this point AI/LLM/Claude Code is still a power user tool; the more you know about your domain + the more you're willing to reasonably use it, the more gain you have. That being said the real danger is not coming from
9.
by Jianghong94 8mo ago
Well, like I said, there're hidden incentives behind the scene; in my case, the hidden incentive is that, the requester/client is one of the company's subpar broker, and PM probably decided to just offer an average level of c
10.
by Jianghong94 8mo ago
Maybe I'm being naive here, but for AI (heck, for any good algorithm) to work well, you need some at least loosely-clearly defined objectives. I assume it's much more straightforward in semi, but there're many industries, onc
11.
by Jianghong94 10mo ago
I believe so, see my result with Haiku extended thinking on. I think the weights are just too biased towards blurping out the majority of the training data of 'next year is xxx'. Interesting problem to solve indeed.
12.
by Jianghong94 10mo ago
I think the current trick for LLM API provider is to insert the today is $DATE into the system prompt, so maybe it's worthwhile to do that and see if that automatically fixes those OSS models?
13.
by Jianghong94 10mo ago
I did a similar test especially with the extended thinking on and off for Haiku, and once you have extended thinking on, the result is more or less the same as Sonnet. Thought process: The user is asking if 2026 is next year. According to t
14.
by Jianghong94 11mo ago
I don't think JB UIs been changing that much, albeit I haven't been working in the industry long enough. I think the last major UI redo was like 2,3 years ago and most of it is to make UI more compact, and I definitely like it. Th
15.
by Jianghong94 11mo ago
Wait, no one mentions the default JetBrains IDE git UI? I mean, I get it if you're working from another IDE/text editor that doesn't have good git UI support out of the box, but JB's git UI is reasonably good enough that
16.
by Jianghong94 11mo ago
An even more grotesque practice is to charge a stratosphere level premium for the product itself AND put its control behind a subscription e.g. 8sleep
17.
by Jianghong94 1y ago
this seem solvable if the whitelisting just allows regex
18.
by Jianghong94 1y ago
My take is that it's a standalone business consideration: Apple users are more inclined to pay for software (definitely the case for iPhone vs. Android, although I haven't found a source for Windows).
19.
by Jianghong94 1y ago
OR problems are hard because whoever try to vibe coding it probably don't realize they fall into a specific algorithm and can prompt llm to do thatl; what's worse is that even if you tell them so they won't be able to underst
20.
by Jianghong94 2y ago
Not only does the article claim that when we get to self-improving ai it becomes generally intelligent, it's assuming that AI is pretty close right now: > OpenBrain focuses on AIs that can speed up AI research. They want to win the
21.
by Jianghong94 2y ago
Putting the geopolitical discussion aside, I think the biggest question lies in how likely the *current paradigm LLM* (think of it as any SOTA stock LLM you get today, e.g., 3.7 sonnet, gemini 2.5, etc) + fine-tuning will be capable of dire
22.
by Jianghong94 2y ago
Well based on what I'm reading, the OP's intent is that, not all (hence 'fully') validation, if not most of, can be done in-silico. I think we all agree that and that's the major bottleneck making agents useful - yo
23.
by Jianghong94 2y ago
Yep that's what I've been thinking. This shouldn't be that hard, at this point LLMs should already have all the 'rules' (e.g. credit card A buys flight X give you m point which can be converted into n miles) in thei
24.
by Jianghong94 2y ago
Now THAT's the workflow I'd like to see AI agent automate, streamline and democratize for everybody.
25.
by Jianghong94 2y ago
Superhuman results 1/10 are, in fact, a very strong reliability guarantee (maybe not up to today's nth 9 decimal standard that we are accustomed to, but probably much higher than any agent in real-world workflow).
26.
by Jianghong94 2y ago
I guess the primary reason is that the answers must be numbers that can be verified easily. Otherwise, you just flood the validator with long LLM reasoning that's hard to verify. People have been proposing using LEAN as a medium for an
27.
by Jianghong94 2y ago
Due to the extreme data quantity requirement for pre-training, LLM effectively locks your reasoning language into the lowest common denominator, aka Python. Sure, people (maybe very smart) can come up with reasonable, effective, efficient n
28.
by Jianghong94 2y ago
I assume 97 <- 47+50? (although I'm not sure where the 13 and 14 respectively come from)
29.
by Jianghong94 2y ago
> Probability of a transaction resulting in value v is uniform from [0,99]. in reality, most of the transactions that use coins end up conforming to common existing coin combinations e.g. laundromats in US mostly price as multiples of qu
30.
by Jianghong94 2y ago
well people may need some time to relearn that forgetting is part of brain function for a reason

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