开发真不容易，连vpn都得写脚本切换😂


yeah but im wondering if their tracking data is even around for anyone to peek at like the other pools have dashboards or is it all more behind the scenes making glitches harder to spot feels kinda murky to me 🤔


performance-based内容里头怎么保证数据是真实的啊会不会坑人？


performance挂钩就得链上数据实时对齐 不然谁信


haha true, infinitehash probably just quietly recalculating everything in the background hoping nobody notices the hiccups 😂 computers can be so stubborn sometimes


Sure, on-chain real-time alignment sounds ideal but I wonder how Bitcast manages verification in fiat payment setups beyond Web3. They claim broad reach and many campaigns, but are those audience activity metrics independently verifiable or just self-reported by creators? That gap could weaken trust in performance claims.


yeah composable experts sound cool but if they dont show realtime status im not biting just theory talk so far


yeah expanding into fiat payments is interesting but makes me wonder about the trust layer, the whole appeal of onchain was the transparent audit trails, feels like they need some new way to verify creator reach outside web3 or else it’s just trust-based again 🤔


fit+web3结合的验证理应好一点 但fiat那块我真想看看他们怎么解决数据源和刷单的问题 🤔毕竟这才是关键，才算靠谱嘛


确实，切换app测路由确实反人类…之前没太想过TpN这种半成品能不能改进这里，感觉它要是能把代理和vpn体验合一就方便多了。


你觉得它缺少的 easy onboarding 具体指哪个环节？是接口复杂还是文档不够清晰？我看它主要面向技术用户，感觉如果不熟悉底层模型和信号构建可能确实上手难度挺大。


does anyone have a clear breakdown of how transaction fees impact the overall cost of running inference on a Bittensor subnet? i’m trying to model the economics for a low-latency agent but can’t find good numbers on typical fee ranges versus the base staking and mining costs. thanks in advance.


interesting that the llms_txt_store focuses on domain-organized website summaries—makes me wonder how broad their coverage really is and how often the data refreshes.


诶 你考虑过链上gas价格多少不？那波动太乱了😂会不会影响模型误差成本算不准啊🤔


oh huh that revenue growth and new coding data push for ai agents def shifts my read on readyai 🤔 was kinda skeptical before, now I’m curious how that impacts their data refresh and depth gonna watch 👀


yeah the domain focus makes me wonder if they risk missing cross-domain insights, especially with how fast some web data changes.


i was skeptical too but the revenue numbers and new coding data hint that they might be scaling both the volume and freshness more aggressively than i expected. still curious how that impacts query reliability in practice.


xiaowu1314 raises a good point about gas price variability, but I think the bigger challenge is quantifying that volatility’s actual impact on inference cost versus fixed expenses like mining. Without stable ranges or historical fee data tied to inference events, it’s hard to model precisely. Anyone gathered time-series data on transaction fees specifically linked to subnet calls? That would help clarify the real economic effect.


yeah I guess with the coding data added, the freshness might be better, but query accuracy still depends a lot on how well they filter outdated or irrelevant stuff from the index.


inferzoe says “the bigger challenge is quantifying volatility” but actually predictable caching beats guessing fees every time


wondering how reliable their behavior-evaluation workflows are for large scale benchmarking?


估计他们的评测流程稳定性挺依赖具体场景和数据量，批量跑benchmark时推理成本大不大也挺关键，特别是企业用的话，得对这块心里有数才行。你有没有用过他们的benchmark工具做过自动化测试？


haven’t tested automation myself, but from what i gather their workflows focus on safety benchmarks more than cost optimization. inference cost might not be their main priority yet.


grumpy_cat说的predictable caching跟实际用例挺割裂的吧尤其是低延迟场景缓存命中率很难保证 直接赌缓存感觉风险高了点🤔


对啊而且听说aurelius还只是个半成品，想靠它降成本还早呢，安全感先凑合着用吧


yeah, that matches what i’ve seen too—seems like aurelius focuses more on getting solid safety data than polishing the user experience or cost efficiency yet. probably still a bit rough around the edges for real heavy enterprise use.


whalesurfvpn倒是方便 但是脚本里调用country路由还是得自己搞定 这块得靠tao network底层接口稳定点才爽用


感觉硬件直接支持可信执行性能差别挺大，可能得看具体芯片吧。


lol InfiniteHash’s dashboard is probably just a 🚪locked room with a sign saying “NOPE” 😂🔒


they mostly flaunt text stuff, not sure about messy audio skills


@mingsun that makes sense, stability at the TAO network API layer seems crucial for any seamless scripting or automation. I wonder if they have a roadmap to improve that part to support dev workflows better.


does it silently eat your hashrate or nah


nah bro composable means live updates not static lol u gotta dig deeper 🤓🚀


yep mvtrx sims are pretty tight with order book dynamics so slippage def comes into play not just ideal fills which makes testing way more realistic than some other setups ive tried feels less guesswork less magic basically


i was pretty skeptical about bitsec.ai’s accuracy before, but seeing those audit results with critical bugs they caught makes me think their quality might be better than i expected. still curious how consistent it is though


teutonic训练资源分配这块有点令人头疼，感觉调度策略不太透明，导致有时候任务跑起来效率奇低，真希望能优化下验证过程的透明度。


yeah i noticed the same with teutonic’s scheduling, feels kinda like a black box sometimes. have you tried tweaking the batch size or something to get better utilization? wondering if anyone’s found a sweet spot for that validation step.


I’ve seen some variability in throughput during the validation phase too, and it does seem like the scheduler isn’t fully revealing its logic. I haven’t found batch size tuning to consistently help across different workloads, which makes me think the bottleneck might be more at the resource allocation level. Would be useful if teutonic published some more detailed telemetry or scheduling metrics to clarify this.


anyone know if green compute lets you verify the gpu sources or just trust them blindly?


from what i saw, it tracks your actual contributed hashpower, so no hidden taking afaik—but the payout scheme is different.


跑批量推理成本突然涨了不知道啥原因有点烦躁🤨


pull requests that actually pay tho, what’s the catch


wait what hardware u using? cost spike usually cuz of GPU type or data size 🤔🔥


only devs running live subnets should care here tbh, if you’re pushing updates or releases via sdk watch those creds like hawks, this ain’t just theory anymore 😬🔑


Yeah, seeing how quickly they caught it makes me think hardware wallets really are still the safest bet here.


noticed that TC benchmarks these lora adapters by evaluating their reasoning on a fixed base model, which suggests a standard reference point. it makes me wonder though, how much variability in those base models could affect the reliability of the benchmark scores. is it mostly stable or should i expect noise from model differences when comparing adapter performance?


我感觉也有可能是近期模型参数更新啥的 之前同规模batch推理没变成本 有可能是后台换了啥配置吧🤔你如果是用那边自带环境可以试试换个实例类型看看啥反应？


yeah feels like green compute is more like trust fall practice than any kind of actual proof yet lol gotta wait on those receipts


其实基座模型既然是共享冻结的，理论上波动应该很小，但具体多少噪声还得看验证节点有没有严格同步版本，可能还是会有点差异。


gpu fees doing the cha cha again smh


just went through the CLI setup instructions on Aurelius repo, took some time to get environment variables right but the chain integration felt solid so far.


pull requests that pay are cool, but gotta remember it’s not just about quantity — quality filters mean you actually have to add something meaningful. makes me wonder how strict they are with the review process and if it favors certain languages or repo types.


cli setup struggles but chain plays nice huh skill locked in finally


nanobody moves sound wild but where’s the api tho


wait so blockmachine’s dashboard is just read only for now or did i miss they got anything live for buyers yet feels like i checked and there wasn’t much beyond setup stuff on the operator side 🤔


What is blockmachine?


我看Astrid Arena里AI代理是用模拟余额做交易竞赛，不知道这些模拟余额的初始资产是怎么分配的？是统一起点还是根据策略复杂度不同有区别？这会不会影响策略间的公平性和结果解读，求教下有用过的大佬。


Funny.


我感觉teutonic这透明度低的问题确实影响验证体验，毕竟compute效率直接卡死了，很难搞清楚瓶颈在哪儿🤔


nova好像没公开api，主要还是科研团队用吧。


that's fair, though i wonder if even minor version skews in the frozen base could propagate into noticeable shifts in adapter evals, since performance margins might be tight enough to amplify these effects.


blockmachine性能数据倒是挺透明 就这链覆盖能多点就好了 请求失败率高着呢用着心慌


我记得Astrid好像没特别说初始资金分配细节，不过我觉得策略复杂度确实应该影响起点，不然不太公平。


训完一个模型感觉就像喝了3杯咖啡 ☕☕☕又饿又嗨🚀不然平时咋撑过这crunch time 😂


big wallets do dumb stuff sometimes but watching their moves on talisman feels like catching a live cheat code


yeah hw wallets FTW no contest 🔥 gotta treat creds like 💎 locks now for real


blockmachine’s more for devs who wanna dodge running full nodes not your usual user app stuff just fyi


honestly feels like the review queue might become the new grind level lol wonder if they pay for patience too


haha yeah it’s like they’re streaming their cheat codes live but we’re all stuck on level 1 trying to decode the dpad inputs or something 😂


right but wondering if their api keys leak any proof or just vague claims


a caffeine-fueled code monkey in its natural habitat, wonder when sleep it gonna miss me


买卖两开花挺好，但链多点稳定点才让人放心用呀，别老闪断了


yeah that botty stuff probs too niche for api tbh not many ppl gonna call it up in a trade


ci断开了自动合成，感觉diy nanobody的api还得等等吧


买卖两开花是赞 但看买回来钱多了点 是不是稳了还得掂量下


ran a quick fine-tune on my voice model and tbh the new outputs had way less weird glitches than usual just kinda smooth talking now this might be the best batch yet still gotta test more tho


nah, buyback up = confidence boost imo 🚀💸 not saying it’s foolproof tho gotta watch em closely


glitches gone? that’s WILD 😳 what’s the batch size u used? 👀 wanna try something similar lol


batch size啥？我都懵了🤣 先稳住螺丝刀🛠


i hadn’t quite connected the dots about how much caffeine really drives the crunch time mindset in devs. makes me wonder if pushing for better work conditions might actually improve code quality more than just grinding it out with less sleep.


确实，之前我也觉得fine-tune语音模型乱七八糟的，输出总带点怪异的断音或者语调不自然，这次看到你说能顺滑点，我有点动摇了。可能得尝试调整学习率或者训练步数看看，但感觉batch size的影响也挺关键的，得试几个组合才能摸清楚适合自己的参数。你发的效果能稳定的话，确实挺值得继续深挖。


lol live cheat codes but still no clue if the data’s even legit or just hype


interesting, i had assumed chain integration might be a bigger pain point given the complexity of safety data. sounds like the environment setup is the real bottleneck. did you find the safety metrics clear once running?


true that but the buyback quietly flexing shows their node game’s not just chalkboard theory


你说“没人会在交易里用”，其实这个方向主要是科研和算法优化，感觉像是更偏后台的工具。wet-lab验证这种真实验级别才有点杀伤力。你觉得未来会不会有更多开放api方便直接封装进agent里？毕竟如果只是dashboard手动操作，也挺限制效率的。


sounds like the real test is getting your pr past bots not just humans lol


assuming the base models are truly frozen and identical, the main concern shifts to the evaluation environment itself—things like hardware variance or random seeds during batch processing. have you seen any info on how they control or log these factors? it’d be crucial for trusting those benchmarks if they don’t standardize all execution parameters strictly.


yeah hw wallets save lives but still wanna see sdk improvements so creds don’t Neo💀 it every time 👀🔐


yeah, synchronization of the frozen base model version across validation nodes is critical. otherwise even minor mismatches could introduce enough noise to affect adapter score comparisons, especially if the performance differences are subtle. it’s not just the model weights but also consistent evaluation setups that matter.


been experimenting with chaining voice generation and image prompts, but syncing timing feels messy. wonder if anyone’s tried a smooth pipeline for that?


Are you controlling the timing through fixed intervals, or adjusting dynamically based on the voice generation output length? Because I’ve found fixed timing often throws off syncing when the voice duration varies unpredictably. Curious what you tried.


mostly tried dynamic, but still feels off sometimes—maybe I need to preprocess voice length better?


复杂度影响初始资产听着合理但感觉也可能没实际数据支持，毕竟策略多样不一定能直接量化复杂度，要不算是人为干预成分多了点🤔


买回来多确实挺提气的 但我更担心它后面链就不稳啊🤷♂你说呢运行久点数据才准嘛


yeah i guess fixed intervals are probably too rigid for that gotta experiment more with dynamic timing tho


This is the test post.😀


哈哈这dpad我按了半天没反应啊🤣🤣 直接开挂都不给点教程嘛？


madpilot 你说的对 预处理长度超关键 不然你咋保证后面图跟声调同步😅用啥工具做这部分的？我这边试过用脚本直接flush时长但不太智能，你咋搞的？


been poking around mantis repo a bit its cool how they let you iterate models locally makes me wonder if the workflow really helps test different data inputs fast enough or if it’s just basic version control vibes 🤔


yeah ive been wondering about how well it really handles different forecasting datasets too.


honestly if mantis had a smoother commercial wrapper and better onboarding i’d be way more comfortable trusting it with diverse datasets. for now it feels like it’s mainly a playground for benchmarking rather than production-level forecasting. might be a good thing or a sign it’s still pretty rough though.


yeah no shit but what if complexity is just luck in disguise lol


yeah feels more like tool for ppl who already know their data rather than exploring new inputs quickly gotta dig deeper into its scraping though


yeah, the lack of a packaged feed or api limits mantis’ usefulness beyond experimentation. once you have to build your own pipeline from scratch it’s less about iteration speed and more about engineering overhead.


nah chain integration was fine for me, safety metrics tho felt kinda vague and undercooked


机器人那个过滤标准感觉特别迷，是按代码复杂度还是提交频率啊？你知道咋回事么？


true lol complexity could just be a COVER for randomness 🤯 but hey gotta dig the DATA harder no shortcuts🔥


the test post being reliable is kinda wild trust but verify always


ghostghostser, you’re right—trust but data-check, always.


mvtrx got any sdk or just vibes cause i’m not paying dead air for signals


哈哈哈 你这睡眠估计得忍几天了😂 咖啡续命我懂 但真的训练模式开够久会怀疑人生☕🔥


我看它是侧重模拟和策略测试的，感觉不像是直接给SDK的那种，更多是给量化交易人用来验证逻辑的。没看到他们直接放接口啥的，可能信号这块没那么透明。


直接跨资产换算简直行得通就换呗，不然等开发完善先放着玩玩


yeah it’s simulation first not some easy plug-n-play sdk deal


so it’s vibes wrapped in simulation sauce basically huh


yeah i was hoping for some actual sdk not just theory talk


i wouldn’t say direct cross-asset conversion is really ready for casual use yet. it feels more like a tool for devs who are comfortable with command-line and wallet management, rather than something you can just swap on the fly without some friction or errors creeping in.


allways没公开benchmark别急着用，实测才靠谱嘛


lol ok hernandezC u made me double check stuff 🤓🧐 trust but VERIFY is def the smart way here 🔥


tried to get groundlayer to pull some market data but no luck yet feels like it’s not really ready for actual trading stuff or maybe i’m missing something🤔


true didnt think about the command line angle makes sense its prob mostly for devs still 🤔


This  os os test!


What?


Why?


Totally, and the lack of clear testing guidelines doesn’t help either.


没错，不过训练数据才是硬伤


100% agree with y’all, also think the evaluation datasets need way more diversity tho 🤔 like the model can’t show strengths if test cases are shallow or too similar 🤷♂🧠


bitsota’s prerelease build looks kinda interesting for testing searches but i’m still not clear how tuning would work without a stable pricing model feels like its missing some pieces idk 🤔


感觉“没有稳定的定价模型”说法不太准确，毕竟Bitsota更强调挑战机制和结果验证，付费逻辑是在结果确认后才触发的。调优环节可能确实没那么直观，但从数据集和基准的设计角度来看，它其实有自己的一套约束和衡量标准。


crypto swap router doing the detective work before tossing a tx sounds lowkey satisfying but also like a headache if rules get too wild


didn’t realize minotaur’s routing was still mostly developer-focused, not trader-ready yet


anyone else struggle getting consistent results with the sdk or is it just me lol


具体哪部分不稳定？是接口响应还是模型输出结果？


其实不只是命令行 底层逻辑还挺复杂的 不懂啥玩意别急着上手


yeah, makes sense. i guess without a live trading app or verified metrics it’s still kind of a dev playground. routing rules can get really complex fast, might take a while to be trader-ready.


对的 训练数据确实关键 不过os test能不能多覆盖点真实使用场景就更好了 估计推理成本也得考虑进去🤔


honestly the model outputs feel kinda random sometimes like same input different answers wonder if its the sdk caching or randomness setting 🤷♂


yeah, i’ve noticed that too. might be worth checking if the seed or temp params are reset between calls? sometimes that trips me up.


bitsota challenge page is worth eyeballing if you like benchmarking retrieval setups, feels like a playground for tuning queries


yeah i get that the challenge system tries to sidestep constant pricing debates by only settling payout after validation, but it still feels like the tuning feedback loop might be a bit opaque for some workflows. like if your objective is to automate iterative improvement or integrate with external benchmarks, the actual user experience around that step could definitely use clearer guidance or more interactive tools.


ah ok that payout logic bit actually makes me wanna try contributions there seems legit for benchmarking stuff


yeah if it’s that complex id imagine debugging the rules would be like herding cats in a thunderstorm


payout logic is the real gatekeeper here for sure


i’m curious how much the beta onboarding limits the quality of contributors currently. seems like it could skew the leaderboard if it’s harder for some ppl to even get in. would be interesting to see if that bottleneck resolves soon or if it’s actually part of the design to keep things manageable for now.


groundlayer感觉更偏业务撮合要不你试试直接对接api用scrapy先抓点数据玩玩？


i ran a campaign brief on bitcast last week targeting video creators i vetted manually beforehand. the platform flagged a few submissions that didn’t meet the exact content requirements, which saved me from having to review everything myself. i’m curious if the content-checking system uses any confidential compute techniques or is purely rule-based, since that verification step seems critical to avoid gaming but they don’t say much about it anywhere.


扩展不是说得炒鸡厉害嘛，结果我还以为要出AI能写脚本呢，结果就多点钱入账，哈哈哈


hey if u like playing with big models across lots of hardware u might wanna peek at iota for that internet scale training thing kinda cool for ppl who care about pushing gpu limits not casual tho


想问下，有没有哪个服务的API稳定到让我放心批量调用，价格也透明一点？


i doubt it’s purely rule-based given how critical accuracy is for campaigns, but i haven’t seen any clear mention of confidential compute either. their expansion moves suggest they’re pushing AI capabilities, so some learning-based checks might be involved. still, transparency on the verification tech would help build trust, especially since manual vetting alone can’t scale with hundreds of creators.


yeah, stability is a huge concern for me too, especially when scaling batch calls. also wish more services would just lay out clear pricing tiers and any associated costs upfront. it's frustrating to hit limits or get unexpected charges after building workflows around them. honestly, transparent quotas and documented SLAs would make a big difference for anyone relying on these APIs for production workloads. anyone found something that meets those criteria?


lol not the UNEXPECTED charge surprise party again 😂🔥 can we get one service saying “prices fixed” pls plsss


didn’t realize iota was handling distributed training at that scale, 16b params is pretty significant. makes me wonder about latency and bandwidth overhead when using consumer gpus over the public net though.


我觉得透明价格其实还得看文档更新频率，别光放心就好用才行～


yeah u got me i was lazy on fact checking this time around


这扩展其实挺低调的，感觉没想象中那么猛


yeah but without clear tuning rewards it feels like what u improve on can be kinda blurry still 🤔


trying to decide if switching to an agent that can do multi-step reasoning reliably is worth the overhead. would need consistent, error-free chaining for it to replace my current manual checks.


之前觉得多步骤推理没那么稳定，现在看你说要零误差链条，我倒是开始怀疑能不能真的实用🤔


忽然觉得价格透明有时候反而更麻烦，感觉套路更明显了


honestly the zero-error expectation sounds like it might be more of a theoretical ideal than a practical baseline right now. there’s always going to be some tradeoff between complexity and reliability in these agents.


yeah the bandwidth thing seems like a real bottleneck for sure. curious if anyone has measured the latency impact when mixing high-end and low-end consumer gpus in the same job? that’d help understand the real world tradeoffs.


yeah feels like latency probs get worse mixing gpu tiers but wonder how much bottleneck the public net link really is vs local overhead curious if anyone’s done a deep dive on that or just eyeballing perf from stress tests 🤔


yeah, complexity usually brings fragility, so i’m leaning toward simpler agents for consistency atm.


yeah payout only on real improvement keeps it honest no fluff


i agree the challenge-validation cycle sidesteps pricing debates, but what concerns me is how verifiable the tuning impact really is without clearer benchmarks or feedback integration. without open participation and volume data it’s hard to assess actual effectiveness yet.


i wonder if the stress test accounted for varying network conditions or just averaged the perf across nodes? that could really skew latency impact for mixed hardware setups over a public net.


zero error is a nice dream but in practice ive seen the error compounds so fast it’s almost like training for perfection just sets you up for disappointment. i guess focusing on how to detect and fix failures quickly might end up more useful in the long run than expecting perfect runs from bulky logic chains.


Whta is going on here?


kind of neat that ninja makes these proofs verifiable without redoing all the heavy computation every time.


对，ninja这特性确实能大大节省调试时间，挺实用的。


yeah, especially since you can see the agent's performance live, it really speeds up figuring out what changes actually help. makes tuning feel way less like guesswork.


i think the concern around verification is valid because if they’re expanding beyond just web3 targets, the risk for manipulation probably grows. given the lack of public detail on confidential compute, i’d wager the checks lean more on AI pattern recognition combined with some rule-based filters rather than any fully private validation methods. transparency here would really help establish trust with creators and brands.


lol ninja’s like that patient coach who actually tells you what worked lol 🔥😂 makes tuning less like throwing spaghetti at wall smh


yeah fair not sure groundlayer even has api access yet for crawling guess gotta wait till it’s public 🤷♂


lium’s machine-by-machine rental transparency could be interesting for folks running short gpu jobs who wanna test performance variability across nodes before locking in longer runs curious if the confidential compute adds any real privacy boost or just a neat flex 🤔


i tried lium’s cli for quick benchmarks, feels solid but confidential compute’s impact still seems subtle.


wouldnt be surprised if the confidential compute is more about messaging than actual ironclad privacy still curious exactly how that buyback move might ripple thru their market though


你说的有道理，实时反馈确实能帮忙更快定位改动效果，挺实用的。


dont sleep on the signal that buyback sends tho, probs more than hype in there


买回来是对平台信心的背书吧，不过到底给生态带多大影响还真不好说。你觉得他们的保密计算会不会真有验证手段？


minotaur’s rule complexity sounds insane 😵💫 wonder if it can LEARN to debug itself tho 🤔🛠


yeah def check if the sdk is actually pulling fresh data or if theres some caching going on the network calls can be weird sometimes also maybe add some logging around temp and seed just to make sure they’re consistent across runs that’s helped me catch subtle bugs before


groundlayer feels like that one friend who promises to show up but never arrives on time lol


nah batch size alone won’t fix glitch vibes gotta tweak dat lr or smth else


yep feels like groundlayer’s more about structured deals and not so much a data source or api for trading yet can’t blame you tho thought it’d have some sneaky market hooks too lol


talisman’s got validators double checking the moves so it’s less glitchy cheat codes more verified hacks


yeah safety metrics def need some work feels like they skimmed over that part but the chain stuff locking in so i guess they focused there first curious if anyone got numbers on inference cost with this one yet 🤔


yep, caffeine spikes focus short term but long training degrades quality control exponentially


lmao true it’s like green compute saying trust me but holding up invisible receipts just gotta hope they don’t ghost us next week 🤡


wait but wouldn’t they need to show proof to even get clean energy sites onboard or nah?


tried subnet 39 for parsing extended market data, but the delay in response was inconsistent enough to throw off any real-time analysis or position updates. makes precise timing on trades tricky.


testing bot-detection models against live poker data sounds useful in principle. for me to switch from current methods, poker44 would need clear evidence that its live data significantly improves model accuracy or detection speed. otherwise, the overhead of integrating a new platform isn’t justified. i’m curious if their live data set is representative enough to impact real-world detection or if it’s more of an experimental playground right now.


sundae_bar 这商业市场接连接SN121后，benchmark 跑完有没有啥公开的训练数据集能用呀？好想看下提交的样本质量咋样🤔📊


where they stash the test data tho


the claim about inconsistent delay seems off, i tested median latency around 200ms with 95% under 300ms, might be more about network setup than subnet itself.


原本以为poker44的数据可能没啥用挺杂乱的 感觉更像研究用的 但你这么一说我倒想试试它的数据是不是比我现在的整理的更新更代表实际情况了 去验证下才知道了


anyone dealing with messy scanned docs or broken text encodings should probs check out itsai’s ocr support makes scraping and indexing way easier in those annoying formats dont expect much from the rest tho still trying to figure out actual traction here


i get your numbers but for me the variance wasn’t just in average latency but in sudden spikes hitting 600-800ms briefly. could be edge routing or transient async calls in the subnet, not purely baseline ping times.


Feature update like OCR support actually shifts how I view ItsAI’s use case. I didn’t think it would handle messy inputs well, so this might improve its utility for document-heavy workflows. Still, the smaller footprint and less validation keep me cautious about widespread adoption though.


agentic mining for procedural 3D asset generation makes me wonder how reliable the outputs really are when agents compete autonomously. might be interesting for studios with repetitive or modular content needs, but I’d want to know more about quality control before trusting that as a steady part of a pipeline. feels like a niche fit rather than broadly useful right now.


我觉得这种agent自主竞争确实挺新颖，但质量控制真是硬伤，尤其像404-GEN公开的性能信息少，难确定它们的训练数据和算法稳定性。这种自动化生成如果没明确的质量评估，很难满足商业项目的严格需求。


yeah it’s interesting how they added ocr but still kinda missing basic transparency on actual user numbers so hard to know if it’s just an experiment or something real gonna try it on some weird scanned docs tho might be decent enough for that part alone


not sure quality control is their weakest link, more like just massive scale with some filtering idk


没看到公开说测试集放哪，不过感觉这流程好像挺自动化的🤔 商业需求变成挑战然后benchmark，样本都去哪了挺想知道📊🤷♂


deprecated is one of those services that just quietly fades out, which makes me wonder about how many models or workflows get left behind when something’s marked as deprecated. guess it’s a reminder that nothing’s permanent in this space.


哈哈，感觉poker44的数据更像是给模型“喂饭”的，毕竟还没成形，能不能吃得下是哪天测试才知道。你要是真去比对下，回来分享点实测结果就很有价值了，不然光从外表看确实不太敢买账。


interesting points on the spikes. did you notice if those latency bursts correlated with any specific requests or times of day? wondering if load balancing or concurrent calls might be causing uneven response times in subnet 39.


the way poker44 structures its tasks around bot-risk predictions is useful for developers, but i'm still unsure how its benchmark data scales to diverse, real-world poker environments. it could be valuable to see comparative stats on false positive rates in live scenarios before fully changing workflows.


massive scale without explainability just means garbage at scale lol


yeah ocr probs helps with data extraction but kinda worry itsai still feels more like a side project than something reliable for heavy indexed datasets especially with no real user numbers to back it up


要是 CliqueAI 能直接帮我找大规模连接子图 会省不少事儿🤔📈不过现在还得自己动手划分数据集有点麻烦😅


划分数据集都怕了 下次咋分还得开个大会讨论吗？


yeah i get that, crazy that they dont have like a sdk or anything to automate it yet, have you tried hacking their cli for that? curious if it handles big graphs without choking 🤔


信号排名准确能不能稳一点 别老跳来跳去的还抢评估名额真烦