2026-07-25 13:20 GMT+8 · summary_2026-07-25_13-20.md
🤖 AI News Summary - 2026-07-25 13:20 GMT+8
Focused AI/dev subreddit roundup.
Full site: https://ai-news-summary.pages.dev/
What changed since last run
- [Paper] Statistically-Lossless Quantization of Large Language Models — r/LocalLLaMA
- slow response time with RAG and utilizing knowledge base. — r/OpenWebUI
- Any recommendations for self-hosted OpenTelemetry monitoring tool which supports SSO? — r/selfhosted
- Made my bricked IoT camera a MCP/API server — r/selfhosted
- Why does it take a year for owui to load? — r/OpenWebUI
- Tibo floated a /slow mode using batch compute two months ago. Would you trade latency for significantly more Codex usage? — r/Codex
- DKV: Open-source KV-cache compression framework for local LLM inference (CLI + technical report) — r/llmdevs
- I don’t know if anyone else has this impression of how Anthropic treats Haiku — r/ClaudeAI
- I want to personally thank Sam Altman for all those resets to celebrate our new lower limits — r/Codex
- Jensen Huan created his official X account just to share his support for open models, an hour ago. — r/openai
- Kimmi K3 vs Claud Code: Kimmi K3 is so honest and says “Join waitlist” instead of taking money and then nerfing models or shady things — r/ClaudeCode
- OpenAI refuses to sign letter supporting Open weight models. — r/openai
r/openai
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | Jensen Huan created his official X account just to share his support for open models, an hour ago. | [Image: Jensen Huan created his official X account just to share his support for open models, an hour ago.] Remember to give him a follow! | 2026-07-24 23:00 GMT+8 | /u/TORUKMACTO92 | Community reaction (frontier/gpt-5.4-mini): Commenters largely agree with the substance of supporting open-weight models, but several say Jensen Huang’s endorsement is strategically self-serving because NVIDIA benefits from more AI companies, more model adoption, and more hardware sales; one defense argues the criticism ignores the actual point of the statement and reduces the discussion to dunking on motive. The main practical takeaway is that open models are seen as healthier for the ecosystem and as a counterweight to closed vendors like OpenAI and Anthropic, especially as those firms are reportedly pursuing their own chips/hardware, which could pressure NVIDIA’s sales mix if the market consolidates around fewer buyers. Overall sentiment — post: mixed; author: critical. Reply threads: 2026-07-24 23:08 GMT+8: post=mixed, author=critical — He says he likes the pro-open-model sentiment but thinks Huang is acting like the guy selling shovels in a… | 2026-07-24 23:53 GMT+8: post=positive, author=neutral — They argue the criticism is just a Reddit dunk and that the real point is that Huang wants more AI either… | 2026-07-25 00:58 GMT+8: post=positive, author=neutral — They note that OpenAI and Anthropic are both working on their own chips, so NVIDIA needs open models to keep… | |
| 2 | OpenAI refuses to sign letter supporting Open weight models. | [Image: OpenAI refuses to sign letter supporting Open weight models.] Do y’all see the irony here, OpenAI was created to bring general intelligence to the masses and now they’ve turned back from their mission all to support Sam Altman’s greed This company is impossible to support. Keeping the name OpenAI is a joke now. | 2026-07-25 06:33 GMT+8 | /u/PsychicorAI | Community reaction (frontier/gpt-5.4-mini): Commenters largely interpret OpenAI’s refusal to sign as a business-motivation issue rather than an ideological surprise: one says it’s about monopoly power, another says the refusal itself is more informative than the letter, and another frames it as expected behavior from firms whose moat is proprietary frontier models rather than open ecosystems. The main disagreement is over whether the post unfairly singles out OpenAI, since replies point out that DeepMind and Anthropic also haven’t signed, and one user calls that framing whataboutism; practical takeaways are that signatories tend to be open-ecosystem companies and that non-signing is not unique to OpenAI. Overall sentiment — post: mixed; author: critical. Reply threads: 2026-07-25 07:07 GMT+8: post=positive, author=neutral — They argue the refusal is explained by business incentives, noting that open-ecosystem companies signed while… | 2026-07-25 06:46 GMT+8: post=critical, author=critical — They question why OpenAI is being singled out when DeepMind and Anthropic also have not signed the letter. | 2026-07-25 07:03 GMT+8: post=neutral, author=neutral — They respond that DeepMind does release open-weight Gemma models, even if not at flagship level, which… |
r/LocalLLaMA
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | [Paper] Statistically-Lossless Quantization of Large Language Models | [Image: [Paper] Statistically-Lossless Quantization of Large Language Models] Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques… | 2026-07-25 02:06 GMT+8 | /u/pmttyji | Community reaction (frontier/gpt-5.4-mini): Commenters mostly read the paper as interesting but not compelling: several said the idea overlaps with their own lab/spare-time research, one thought the abstract underplayed layer-by-layer quantization, and another called the work “not super interesting” compared with an Unsloth blog post. The strongest pushback was that “near lossless” sounds like marketing, the paper lacked code/comparison evidence, and one commenter claimed the method ended up worse than 4-bit GPTQ; practical takeaways are to verify against GPTQ/AWQ-style baselines and inspect how reconstruction loss, KLD, and any internal-state effects translate to real token quality. Overall sentiment — post: critical; author: neutral. Reply threads: 2026-07-25 02:38 GMT+8: post=critical, author=neutral — They argued that “near lossless” is still lossy, complained there was no code or fair comparison to current… | 2026-07-25 03:48 GMT+8: post=skeptical, author=neutral — They said the abstract should have differentiated layer-by-layer quantization and, after reading further,… | 2026-07-25 03:47 GMT+8: post=concerned, author=neutral — They said they did not understand how the paper could report a recovery rate over 1.0. |
r/llmdevs
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | DKV: Open-source KV-cache compression framework for local LLM inference (CLI + technical report) | [Image: DKV: Open-source KV-cache compression framework for local LLM inference (CLI + technical report)] Hi everyone! Over the past five months I’ve been working on DKV (DifferentialKV), an open-source project exploring KV-cache compression for long-context local LLM inference. | 2026-07-25 11:44 GMT+8 | /u/Om_5000 | Community reaction (frontier/gpt-5.4-mini): Commenters view the anchor-based differential KV-cache idea as a clever, more sensible alternative to bluntly chopping context when memory balloons in local LLM setups. The main caveat is validation: one commenter wants CUDA testing against a standard full-context run on an A6000, and the author says CUDA GPUs were unavailable so they published now and plan to add CUDA validations in a later version, likely after securing grants. Overall sentiment — post: positive; author: positive. Reply threads: 2026-07-25 11:51 GMT+8: post=positive, author=neutral — They praise the anchor-based approach as smarter than simply truncating context to save memory, and they ask… | 2026-07-25 12:02 GMT+8: post=positive, author=positive — They say they want to try the idea, but lack access to CUDA GPUs, so they published first and plan to seek… | |
| 2 | TERSE specification, memory, brain and state for your LLM. All FOSS and very token/tool efficient* | [Image: TERSE specification, memory, brain and state for your LLM. All FOSS and very token/tool efficient*] What is TERSE? | 2026-07-25 04:41 GMT+8 | /u/Defiant-Juice-2745 | Community reaction (frontier/gpt-5.4-mini): Commenters like the node-graph/brain visualization and the idea of more explicit state retrieval, but one immediately warns that pitching TERSE as a new AI standard feels like previous “JSON/REST of AI” attempts that came and went. The main caveat is validation: they want to see it stressed on messy real-world data, while the author-side reply argues TERSE is a Karpathy Brain OOTB variant that collapses many wiki files into one brain.terse file and claims fewer tokens/tool calls via one- or two-hop parallel retrieval. Overall sentiment — post: mixed; author: neutral. Reply threads: 2026-07-25 09:47 GMT+8: post=skeptical, author=neutral — They think the concept sounds neat and the node graph visualization is interesting, but they are wary of yet… | 2026-07-25 10:19 GMT+8: post=positive, author=neutral — They clarify that the graph UI is not novel, saying it is Karpathy Brain OOTB with many wiki files replaced… |
r/OpenWebUI
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | slow response time with RAG and utilizing knowledge base. | I’m currently using Open WebUI with a knowledge base made up of text files, and the responses are accurate, but they’re taking quite a while to generate. I plan to keep adding more text files to the knowledge base, so I’m wondering what the best way is to improve response speed as it grows. | 2026-07-25 07:57 GMT+8 | /u/boss28984 | Community reaction (frontier/gpt-5.4-mini): The commenters converged on the idea that the slowdown is probably not the 15-file knowledge base itself, but the model/runtime path: one person first suggested slow token generation on weak hardware, and the follow-up landed on the Jetson AGX Orin as the likely bottleneck. The practical takeaway was to stop tuning Open WebUI RAG knobs like top_k and context window as a first fix, and instead try a stronger model or an external API connected to OpenWebUI; there was no real disagreement beyond initial uncertainty about whether the lag was from retrieval or generation. Overall sentiment — post: concerned; author: neutral. Reply threads: 2026-07-25 08:03 GMT+8: post=concerned, author=neutral — They suggested the latency may come from slow token generation because the LLM is running on weak hardware. | 2026-07-25 08:05 GMT+8: post=neutral, author=neutral — They said the system runs on a Nvidia Jetson AGX Orin, noted that Claude’s advice to tweak top_k and context… | 2026-07-25 10:42 GMT+8: post=positive, author=neutral — They concluded the model itself is the bottleneck, recommended connecting another model API to OpenWebUI, and… | |
| 2 | Export chat no longer including all details? | [Image: Export chat no longer including all details?] I noticed that in v0.10, if you click any of the export chat options in the top right corner of the chat UI (Download or Copy), it no longer includes all details like tool call inputs and outputs and so on. Does anyone know if this is moved to a setting somewhere… | 2026-07-23 17:02 GMT+8 | /u/RangingBloyster | ||
| 3 | Seltz.ai | Appreciate your help providing info on how to use Seltz.ai (http://Seltz.ai) as a node for web search. | 2026-07-24 01:16 GMT+8 | /u/dotanchase | Community reaction (frontier/gpt-5.4-mini): The dominant practical takeaway is to use searxng instead of Seltz.ai: one commenter says they run searxng in a container for free, another says it works very well, and a third recommends pairing it with Crawl4Ai for deep crawl and structured data for LLMs. The only real disagreement is whether Seltz.ai offers anything beyond that baseline, with one comment explicitly asking if the post is an ad and saying to stick with searxng unless Seltz provides something searxng cannot. Overall sentiment — post: skeptical; author: skeptical. Reply threads: 2026-07-24 01:24 GMT+8: post=skeptical, author=skeptical — They question whether Seltz.ai is a paid product and recommend searxng in a container as a free alternative… | 2026-07-24 11:53 GMT+8: post=neutral, author=neutral — They joke that companies are being trained on their search results and say the web search providers are… | 2026-07-24 05:36 GMT+8: post=positive, author=neutral — They say searxng works very well and that combining it with Crawl4Ai provides deep crawl plus structured data… | |
| 4 | Why does it take a year for owui to load? | [Image: Why does it take a year for owui to load?] I feel like I sit and stare at the screen a lot is there anyway to speed this up? | 2026-07-25 01:24 GMT+8 | /u/thewhzrd | Community reaction (frontier/gpt-5.4-mini): The main consensus is that slow OpenWebUI startup is usually caused by one stale or unreachable backend connection, especially an Ollama or other OpenAI-compatible endpoint that OWUI fans out to while building the model list, so the UI waits on a connect timeout rather than on genuinely slow storage. Commenters give practical debugging steps: check the Admin panel connections, inspect browser DevTools Network for the slow models request, and look at container logs; one operator also suggests AIOHTTP_CLIENT_TIMEOUT_MODEL_LIST=1 when endpoints are intentionally flaky, while another notes firewall/network rules can also be the culprit. Overall sentiment — post: positive; author: neutral. Reply threads: 2026-07-25 01:31 GMT+8: post=positive, author=neutral — They say their own slow load was caused by an Ollama endpoint that had been taken offline, and that OWUI kept… | 2026-07-25 01:56 GMT+8: post=positive, author=neutral — They confirm the same pattern and add that the failing connection also shows up in the logs, including Docker… | 2026-07-25 02:57 GMT+8: post=positive, author=neutral — They explain how to identify the blocker in DevTools Network after a hard reload, noting that a constant ~5,… | |
| 5 | Working with files, tools and LDAP | I have Gemma 4 running locally and external ones, I’m trying to make it more useful inside OpenWebUI. A couple of things I’m looking for advice on: - Document analysis / file workflows I want to be able to upload PDFs and DOCX files, have the model analyze them, and ideally generate edited or new documents back. | 2026-07-23 20:43 GMT+8 | /u/Lazy_Secretary_3091 | Community reaction (frontier/gpt-5.4-mini): Commenters largely agree that OpenWebUI can ingest PDFs/DOCX, but its built-in RAG is better for retrieval than for full-document reasoning because it chunks/embeds instead of handing the model the whole file, so several recommend converting to Markdown first or pasting extracted text for serious analysis. For round-tripping edited files, they converge on external tooling: open-terminal for direct file manipulation, or Markdown plus Pandoc/xBerg MCP tools to turn outputs back into DOCX/PDF, with OCR/text-PDF filters and token cost noted as practical caveats. Overall sentiment — post: positive; author: neutral. Reply threads: 2026-07-23 21:32 GMT+8: post=positive, author=neutral — They suggest open-terminal integration as the most straightforward way to let the model work directly on… | 2026-07-23 22:05 GMT+8: post=positive, author=neutral — They say PDFs and DOCX can be uploaded directly in OpenWebUI, but content analysis is better if the file is… | 2026-07-23 23:44 GMT+8: post=positive, author=neutral — They clarify that the chat window plus sign can upload text, PDF, and Markdown files when file upload is… |
r/selfhosted
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | Any recommendations for self-hosted OpenTelemetry monitoring tool which supports SSO? | Hey all, I’m looking for a way to monitor my applications using OpenTelemetry but with a few caveats: - Fully self-hosted - SSO / OIDC support so I can use my PocketID instance - No account creation needed on third party servers I’ve already done quite a bit of digging but it seems like a lot of tools out there either… | 2026-07-25 04:23 GMT+8 | /u/LightningPark | Community reaction (frontier/gpt-5.4-mini): The clear consensus was that Grafana is the default self-hosted answer for this use case: commenters said OSS Grafana supports OIDC via auth.generic_oauth in grafana.ini, handles RBAC and automatic account creation on sign-in, and can sit on top of the usual OTel stack (collector, Tempo, Prometheus or Mimir, Loki). The main disagreement was not about Grafana itself but about where to terminate auth: one commenter recommended turning auth off for a single-user deployment and relying on a proxy or Tailscale, while another argued the more general pattern is to put OIDC at the edge with oauth2-proxy, Authelia, or Caddy forward_auth so apps like SigNoz, OpenObserve, and HyperDX do not need native SSO. Overall sentiment — post: positive; author: neutral. Reply threads: 2026-07-25 05:02 GMT+8: post=positive, author=neutral — They asked why Grafana would not work and pointed out that it is the standard self-hosted monitoring… | 2026-07-25 05:06 GMT+8: post=positive, author=neutral — They suggested that for a single-user setup you can disable auth entirely and gate access with a proxy or… | 2026-07-25 05:15 GMT+8: post=positive, author=neutral — They said Grafana OSS has generic OAuth/OIDC support in the auth.generic_oauth block, clarified that SAML is… | |
| 2 | Made my bricked IoT camera a MCP/API server | [Image: Made my bricked IoT camera a MCP/API server] I have 5 Azure IoT Starter Kit cameras, the vendor (Altek) stopped updating their firmware and the last firmware doesn’t work with Azure anymore. I emailed Altek to help me with the firmware or any SDK so I can use the camera in a different ways. | 2026-07-25 00:48 GMT+8 | /u/ahstanin | Community reaction (frontier/gpt-5.4-mini): Commenters mostly reacted as operators looking for practical ways to use AI/OpenCode to probe bricked or closed-vendor cameras, asking whether Hermes was used, how to get started, and what tooling stack managed the workflow. The main caveat surfaced by the author was that the hardest part was hardware-level debugging—finding the right sensor voltage and signal because the manual named the wrong sensor alias—and he said he wrote the code while checking whether it can be shared legally. Overall sentiment — post: positive; author: positive. Reply threads: 2026-07-25 01:07 GMT+8: post=positive, author=positive — They said they had old bricked Amazon cameras too and asked whether something like Hermes was used to probe… | 2026-07-25 02:48 GMT+8: post=neutral, author=neutral — They asked about a multimodel-agent stack using GLM, Kimi K3, ChatGPT, and Fable 5 as manager/proof reader,… | 2026-07-25 03:17 GMT+8: post=neutral, author=neutral — They asked whether OpenCode or another tool was used to manage the probe workflow, showing interest in the… |
r/ClaudeAI
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | I don’t know if anyone else has this impression of how Anthropic treats Haiku | [Image: I don’t know if anyone else has this impression of how Anthropic treats Haiku] Is Haiku the Meg Griffin of the Claude family? | 2026-07-25 07:40 GMT+8 | /u/gabrielsnofall | Community reaction (frontier/gpt-5.4-mini): Commenters largely agree with the premise that Haiku is the least prominent Claude model, with several saying they almost never use it and default to Opus or Sonnet instead. The main operational split is between those who see Haiku as only worth it for cheap automated bulk calls or niche tasks, and one commenter who says its value is that it appears to stay comparatively untouched by guardrails, making it usable for requests the main models cannot answer; a practical takeaway is that teams already using Claude are routing planning to Opus and execution/subagents to Sonnet, not Haiku. Overall sentiment — post: mixed; author: neutral. Reply threads: 2026-07-25 08:26 GMT+8: post=neutral, author=neutral — They say they have trouble knowing when to use Haiku and default to running everything on Opus, asking others… | 2026-07-25 08:48 GMT+8: post=positive, author=neutral — They recommend having Opus plan the work and then spawning subagents during execution, noting that this setup… | 2026-07-25 08:57 GMT+8: post=critical, author=neutral — They argue Haiku is basically never worth using except for very cheap automated AI calls, because introducing… | |
| 2 | Opus 5 results are really shocking!! | Here’s the verdict: - Literally the BEST at long-horizon task. - Using it in Low effort is extremely cost efficient, and gives amazing result (using Sonnet 5 at High is worse than using Opus 5 at Low). | 2026-07-25 03:28 GMT+8 | /u/swapnoneel123 | Community reaction (frontier/gpt-5.4-mini): Commenters largely agree that Opus 5 Low is unusually token-efficient and strong for long-horizon or agentic work, with multiple people saying it outperforms Sonnet 5 High and even Opus 4.8 High in their workloads. The main dispute is not capability but Anthropic’s limits: one user says a book workflow hit a 2-hour session lockout by Chapter 7, while others report only ~2% weekly usage growth or 13% of a 5-hour window after an hour, so the practical takeaway is to test Opus 5 Low for complex orchestration but watch session-window consumption closely. Overall sentiment — post: positive; author: neutral. Reply threads: 2026-07-25 03:56 GMT+8: post=positive, author=neutral — They say Opus 5 Low easily handles long-horizon tasks while using far fewer limits, and that it has replaced… | 2026-07-25 04:07 GMT+8: post=positive, author=neutral — They report that Opus 5 feels highly token efficient, since several heavy dynamic workflows and sessions only… | 2026-07-25 05:26 GMT+8: post=positive, author=neutral — They say usage appears lower than with 4.8 High, and after about an hour across two long-range agentic… |
r/ClaudeCode
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | Kimmi K3 vs Claud Code: Kimmi K3 is so honest and says “Join waitlist” instead of taking money and then nerfing models or shady things | [Image: Kimmi K3 vs Claud Code: Kimmi K3 is so honest and says “Join waitlist” instead of taking money and then nerfing models or shady things] The difference between how Anthropic (and how American AI companies) does business vs how Chinese Kimmi K3 (Moonshot AI) does the same couldn’t be more stark. Anthropic (and… | 2026-07-25 00:02 GMT+8 | /u/simple_explorer1 | Community reaction (frontier/gpt-5.4-mini): Commenters mostly push back on the post’s romanticized contrast: a former Kimi subscriber says Moonshot took his money for months, then mostly returned overload errors and only worked about one-third of the time, while another says Kimi and DeepSeek only stopped accepting payment because they could not keep up with demand and were already giving API errors and timeouts. A smaller thread argues the core operator reality is that AI inference burns real compute per user, so “sold out” or “join waitlist” messaging is more transparent than silently nerfing models, with one user adding that OpenAI feels more generous with resets than Anthropic. Overall sentiment — post: critical; author: critical. Reply threads: 2026-07-25 00:23 GMT+8: post=critical, author=critical — The commenter says Moonshot took his money for several months, mostly returned overload errors, and only… | 2026-07-25 00:30 GMT+8: post=positive, author=positive — The commenter argues that Kimi stopping paid signups is an honest reflection of capacity limits and says… | 2026-07-25 00:39 GMT+8: post=critical, author=critical — The commenter says Kimi and DeepSeek were already taking money while delivering API errors, timeouts, and… | |
| 2 | Opus 5 - immediate disappointment | If you thought you’d be able to do a security review on your own network that you couldn’t do with Fable, think again. 20 minutes in - Found something significant Opus 5 safeguards flagged this message. | 2026-07-25 01:33 GMT+8 | /u/Shot_Whereas_1809 | Community reaction (frontier/gpt-5.4-mini): Commenters largely validate the core claim that frontier-model guardrails make them poor fits for adversarial security work, with one user arguing that serious secops will push teams toward Chinese open weights and citing the Hugging Face incident where US frontier models refused incident-response logs while self-hosted GLM-5.2 was usable. The main caveat is operational economics: several replies shift from policy/guardrail complaints to the realities of hosting large local models like Kimi K3, citing costs such as $300k per half, 100 concurrent users on an NVL8 B300 being unrealistic, custom power/cooling needs, and poor sustained throughput/latency even when a $600k system can run the model. The thread is mostly supportive of the underlying concern, but much of the rest is joking or anecdotal rather than a direct rebuttal, so the practical takeaway is that local/open-weight options may be more workable for sensitive workflows, yet only if operators can absorb substantial infra and utilization costs. Overall sentiment — post: concerned; author: neutral. Reply threads: 2026-07-25 01:49 GMT+8: post=positive, author=neutral — They argue that serious adversarial secops will end up on Chinese open weights, citing Hugging Face’s… | 2026-07-25 01:52 GMT+8: post=positive, author=neutral — They call the situation a disturbing but true reversal and joke about splitting the cost of hosting Kimi k3… | 2026-07-25 02:38 GMT+8: post=neutral, author=neutral — They say shared hosting for Kimi k3 would be extremely expensive, citing about $300k per half, 100 concurrent… |
r/Codex
| # | Post | Summary | Time | Score | Author | Community reaction |
|---|---|---|---|---|---|---|
| 1 | Tibo floated a /slow mode using batch compute two months ago. Would you trade latency for significantly more Codex usage? | [Image: Tibo floated a /slow mode using batch compute two months ago. Would you trade latency for significantly more Codex usage?] I would use this constantly for large tasks where I do not need an immediate response: overnight implementations, large refactors, test generation, long reviews, etc. | 2026-07-25 12:01 GMT+8 | /u/Outrageous_Buyer3095 | Community reaction (frontier/gpt-5.4-mini): Commenters mostly support a slower, lower-priority mode for unattended Codex work, saying they would use it for overnight refactors, bug-fixing, test generation, long reviews, and other jobs where turnaround matters less than consuming more quota or getting a discount. The main disagreement is implementation: one camp says this is basically already how API Flex works, while another argues literal Batch API semantics would be awkward for agentic coding because tool calls are sequential and each inference depends on the previous result; one skeptic also says normal mode is already as slow as possible. Practical takeaway for operators is that a “slow mode” looks useful if it preserves the request/response loop and just lowers priority, but it should not queue the whole editing workflow like a bulk batch job. Overall sentiment — post: mixed; author: neutral. Reply threads: 2026-07-25 12:09 GMT+8: post=skeptical, author=neutral — They argued the proposal is redundant because users either wait for reset or wait for inference, and said… | 2026-07-25 12:20 GMT+8: post=positive, author=neutral — They said the feature already exists for API users as Flex, which sends work to batch compute and is cheaper… | 2026-07-25 12:49 GMT+8: post=skeptical, author=neutral — They questioned whether queueing code editing this way would be efficient, suggesting it may fit translation… | |
| 2 | I want to personally thank Sam Altman for all those resets to celebrate our new lower limits | Running out of tokens makes me feel like I have AGI in my own head! | 2026-07-25 07:54 GMT+8 | /u/9gxa05s8fa8sh | Community reaction (frontier/gpt-5.4-mini): Most commenters agree that OpenAI-style usage budgets have tightened sharply, with one user saying CCusage shows a 4x cut versus February and another claiming the five-hour limit would now cause timeouts in about twenty minutes, so the practical operator takeaway is to expect much less runway for long sessions. Several comments speculate that metering changed from tokens to API-cost-based accounting in 5.6, using the 272k-token long-context double-charge as evidence, but that theory is not confirmed in-thread. There is some disagreement in observed impact because one Max 5x user says their weekly budget only fell 2% on Opus 5, and one commenter broadens the complaint by noting Anthropic’s Opus 5 launch came without any reset at all. Overall sentiment — post: critical; author: neutral. Reply threads: 2026-07-25 08:09 GMT+8: post=critical, author=neutral — They say Sam and Tibo “pulled a fast one” and that OpenAI cut usage by roughly 4-5x, which they describe as… | 2026-07-25 09:55 GMT+8: post=critical, author=neutral — They argue the reduced usage makes the five-hour limit absurd and claim the limit was removed purely to hide… | 2026-07-25 11:06 GMT+8: post=critical, author=neutral — They say CCusage shows a 4x drop versus February, but their own Opus 5 on Max 5x has only consumed 2% of the… |
Generated 2026-07-25 13:20 GMT+8 | Next update in 2 hours