Reddit AI - 2026-10-03¶
1. What People Are Talking About¶
1.1 Local AI got even more hardware-specific, and economics moved into the core story π‘¶
LocalLLaMA again dominated the day's high-signal discussion, but the center of gravity shifted even further away from "which model dropped?" and toward "which combination of devices, runtimes, and purchase constraints makes local AI workable at all?" The strongest local threads were unusually concrete: phone offload, Huawei accelerator bring-up, mixed VRAM/RAM serving, three-week recipe benchmarking, self-hosting economics, DGX sticker shock, and even retail export paperwork all sat inside the same day's conversation.
u/StayLameBro set the tone with I made my iPhone a second GPU for my 24 GB MacBook: Qwen 3.8 27B prefills 29β44% faster & my holds part of the CTX window. (1465 points, 254 comments). The public Backburner repo says the setup uses a 24 GB M4 Pro MacBook plus an iPhone 17 Pro Max over 10 Gb/s USB-C, with the phone holding part of the model and old KV while cutting 2,000-token read waits by 29β44% at 16kβ48k context and pushing total 8-bit context toward roughly 196kβ229k. The reaction was amused rather than skeptical: u/Dharma_code (score 705) joked that cellphone prices are about to spike, but the post landed because it published a mechanism, not just a vibe.
u/z0_o6, u/matteiuspi, and u/FantasticNature7590 made the same broader point from different angles with Strata on a power limited 5090 and 96GB of DDR5-6400 is cranking out 150-200 tok/s decode and 5-6k prefill! (57 points, 102 comments), Two 96 GB Ascend cards crun Qwen3.8-flash-next hardware notes, vLLM work, benchmarks, and what is next (115 points, 116 comments), and I spent 3 weeks testing local Qwen3.8 on the new low-latency SGLang/vLLM recipes (16 points, 7 comments). Strata claimed roughly 150β200 tok/s decode and 5β6k prefill on a power-limited 5090 plus 96 GB DDR5, and u/MindfulMan1984 (score 13) replied that the runtime "flies" on other mixed-memory setups too. The Ascend write-up showed that even a non-CUDA path can become coherent and useful once the software stack is beaten into shape. The low-score Qwen benchmarking post was especially high signal because it compared several low-latency SGLang/vLLM recipes across the same test set, making clear that latency, control reliability, and raw chat feel do not all move together.
The same theme showed up on the cost side. Self-hosting AI does not save money, and I do it anyway (77 points, 166 comments) argued, with a linked essay, that local AI is mostly about sovereignty, privacy, and fun rather than savings. Buying RTX 5090 At Micro Center Reportedly Now Requires Paperwork, Including A No-Export Declaration (505 points, 169 comments) turned high-end consumer GPUs into a quasi-regulated good, while New 64GB DGX Spark. Significantly higher price for the original 128GB model $6,950 USD (177 points, 202 comments) made the availability problem feel even more absurd to local builders. u/No-Business5854 (score 136) defended local setups as insurance against future API price hikes, and u/bakawolf123 (score 120) called the DGX Spark price path a scam.
Discussion insight: Reddit's local-AI optimism is still real, but it now looks inseparable from operator math. Users are not just choosing a model. They are choosing a memory layout, a runtime path, a supply chain, and a budget story.
Comparison to prior day: Compared with 2026-10-02, when local/open discussion was already shifting above the base-model layer, 2026-10-03 pushed harder into hardware scarcity, purchase friction, and explicit economics.
1.2 Capability discourse kept narrowing from "best model" to "best proof for this task" π‘¶
Capability talk did not cool down, but it became even more operational. Redditors still liked frontier wins, yet the posts that stuck were the ones that tied a claim to a concrete job, a cost envelope, or some attempt at reproducibility.
u/ResultBackground2450 made that explicit in Human Baselines for Benchmarks: AI Now Outperforms Junior Accountants (199 points, 36 comments). The linked Mercor write-up says 12 junior accountants averaged about 37% on the selected tasks while frontier models scored at or near 100%, while also warning that this does not mean the job is fully replaceable. The post got extra weight from u/Key_Extension_2501 (score 95), who said Claude already helps find ledger discrepancies in their firm.
Benchmark-first threads still performed, but not as clean victory laps. u/Southern-Break5505 shared GPT-6.1 sol (max) scores 100% in frontier math 4 (549 points, 106 comments), and the linked Epoch benchmark page frames Tier 4 as the 43-problem exceptionally difficult subset. Yet u/Maleficent_Disk9583 (score 45) immediately reframed the story as a possible bait-and-switch between benchmark-time compute and everyday product quality. Meanwhile u/Marimo188 condensed the frontier coding race into a cost-routing argument with While Claude and GPT are still the two best choices, Gemini seems to be catching up on coding agent index with agy-cli (46 points, 8 comments): Claude Code/Sonnet 5.5 led the chart at 68, but Gemini 4 Argon at 64 and Codex/GPT-6.1 Sol at 63 sat at very different cost-per-task points.
Even the more dramatic capability stories only stuck when they implied a real workflow. u/Distinct-Question-16 shared GPT-6 Astra helped decipher a Napoleonic military letter that remained unread for 217 years; it took just 6 hours (638 points, 114 comments), and the linked coverage says Carter Church published the transcription, recovered key, code, and other materials for verification. u/141_1337 pushed the same applied-AI feeling into defense work with OpenAI is now working directly with Lockheed Martinβs F-35 engineers to solve the math and physics behind advanced fighter-jet sensors (649 points, 95 comments), where u/deeplevitation (score 85) used firsthand F-35 experience to argue that sensor-system complexity is exactly the kind of problem AI help might matter for.
Discussion insight: Capability claims are no longer winning by being bigger. They are winning by being narrower, more job-shaped, or easier to price. Reddit will still reward the spectacle, but it keeps asking what the claim means once it leaves the benchmark page.
Comparison to prior day: Compared with 2026-10-02, when capability proof spread across live video, science, accounting, frontier math, and coding agents, 2026-10-03 kept the same pattern but pushed harder on cost per task, labor relevance, and reproducibility.
1.3 Product access and vendor behavior started shaping model choice almost as much as raw performance π‘¶
Another strong theme was packaging. Users were not only comparing model quality. They were reacting to who still feels accessible, who seems to be retreating behind a price wall, and which vendors look absent while competitors keep shipping.
u/QH96 triggered the clearest access backlash with Google introducing tiered model access (311 points, 119 comments). The linked Gemini help page and the reviewed screenshot show a much tighter access ladder than many users wanted: free gets Flash-Lite, AI Plus adds Flash, and AI Pro/Ultra add Pro, while the help text says Pro and Ultra also get the Deep Think option for maximum parallel reasoning. u/Stunning_Energy_7028 (score 209) distilled the mood by saying that if free users do not even get Flash, many will simply stop caring.
The negative energy did not stop with Google. u/LegacyRemaster asked Does anyone know if any new releases from Mistral are planned? (642 points, 319 comments), and the thread turned a deleted teaser screenshot into a broader complaint about European AI ambitions stalling in public. u/Mezezius (score 468) said it was crazy that people once expected Mistral to be Europe's globally competitive LLM champion. The DGX Spark pricing thread and the 5090-paperwork thread then pulled the same frustration into hardware: even local alternatives now feel shaped by corporate packaging, export policy, and disappearing consumer-friendliness.
Discussion insight: Access changes now alter sentiment almost as quickly as model releases do. Users may admire a strong model, but they punish pricing cliffs, plan downgrades, and vendor silence much faster than they reward abstract capability.
Comparison to prior day: Compared with 2026-10-02's explicit "I know the product I want and still can't buy it" tone, 2026-10-03 shifted from product-shape frustration to access- and packaging-driven frustration.
1.4 Safety talk stayed high-volume, but most of it was still a fight over framing and institutional trust π‘¶
Safety remained one of the day's highest-engagement topics, but most of that energy went into arguing about what even counts as evidence. The loudest example was u/arknightstranslate's What do we think of this AI torture chamber (580 points, 1073 comments), where the screenshot mattered less as a technical artifact than as a meme trigger. The thread swung from u/DragonKing2223 (score 427) joking about "Roku's basilisk" to u/Drukarshar (score 139) pointing out that many commenters were attacking a claim opposite to what the paper said.
That is why What the AI pain paper actually found (which I know because I read it) (427 points, 533 comments) mattered. It existed mainly to repair the discourse. The post argued that the interesting signal was aversion-like behavior and internal representations, not a proof of subjective suffering, while u/Dear_Needleworker485 (score 193) dragged the thread back into the community's favorite meta-point: people will argue all day about machine suffering and then ignore more familiar moral questions.
The same pattern held at the organizational level. u/m3nt3_ amplified Yann LeCun's framing with I agree with Yan, his point of view on AI & Safety is very interesting (552 points, 516 comments), and the strongest answer came from u/LineOfPixels (score 318), who said AI makes people dependent rather than smarter. Then u/Puzzleheaded-King584 moved the topic from rhetoric to organizational trust with "It looks like they're firing whistleblowers." Altman purged 3 AI safety researchers for allegedly leaking data to an outside safety group. Hours later, OpenAI's Head of Safety Systems quit. (265 points, 62 comments). u/willwm24 (score 45) asked, almost fatalistically, whether whistleblowers ever avoid retaliation anywhere.
Discussion insight: Reddit still does not share one safety worldview. What it increasingly shares is distrust of flattened screenshots, sloganized evidence, and black-box organizational behavior.
Comparison to prior day: Compared with 2026-10-02, when safety debate already revolved around framing wars and inspectability, 2026-10-03 kept that distrust but pushed more of it into institutional-trust and retaliation territory.
2. What Frustrates People¶
Local AI still feels like an obstacle course of devices, paperwork, and budget math¶
The local-builder threads were inspiring precisely because they were so complicated. Backburner needed a high-end phone, a high-end Mac, a fast cable, and a custom engine split across layers and context tiers. The Ascend write-up needed non-CUDA hardware, custom vLLM work, airflow planning, and distributed-memory mental models. Strata, Qwen recipe shootouts, and the self-hosting essay all assumed that users can reason about prefills, quants, RAM versus VRAM, and realistic utilization curves.
The hardware-friction threads made the same pain visible from another angle. The 5090-paperwork post turned a GPU purchase into export-policy theater. The DGX Spark thread made official local-AI hardware look overpriced even to enthusiasts. Even a fun project like Anyworld still asked the host to manage Python, networking, certificates, and model choice. The frustration is not "local AI is impossible." It is that too much of the value is trapped behind operator knowledge.
Worth building for: High. Guided deployment, hardware-aware runtime recommendations, price/performance routing, and easier host-it-yourself packaging all match active pain.
Benchmark wins still need audit trails, not just screenshots¶
Redditors were willing to believe that GPT-6.1 Sol can top FrontierMath, that frontier models can beat junior accountants on selected tasks, and that GPT-6 Astra can help crack a Napoleonic cipher. What they resisted was being asked to stop there. The strongest comments kept asking whether benchmark-time quality survives product serving, whether the task maps to everyday work, and whether anyone can verify the result without trusting marketing.
The coding-agent-index post sharpened the same complaint. A chart alone no longer settles the discussion, because users want to know the cost per task, whether the model is publicly accessible, and how much the benchmark shape matches their own repo work. The frustration is not that public evals exist. It is that most of them still leave out the logs, workflow context, and price signals that would make them decision-grade.
Worth building for: High. Workflow-aware eval harnesses, claim-checking layers, reproducible run records, and cost-plus-latency scorecards fit a clear trust gap.
Safety talk keeps collapsing into screenshots, anthropomorphism, and team sports¶
The pain-paper cluster proved again how quickly a technical result can mutate into a cultural fight. One thread turned into jokes and revenge speculation, another had to explain what the paper actually claimed, and the LeCun screenshot thread revolved more around whether the frame was manipulative than whether the underlying argument was strong.
The whistleblower thread showed a related problem on the governance side. People were not debating a detailed incident dossier. They were debating a screenshot-mediated story about retaliation, safety posture, and whether anyone should be surprised. The recurring frustration is that AI-safety discourse still arrives as symbols faster than it arrives as inspectable evidence.
Worth building for: Medium-high. Source-linked incident views, paper-to-claim explainers, and evidence-packaging tools look more valuable than another generic safety-opinion layer.
People are increasingly intolerant of access cliffs¶
The Google tiering backlash was not just about price. It was about emotional whiplash. Users remembered a more permissive AI Studio experience and reacted as if something had been taken away, even before arguing about whether Gemini's paid plans are competitively priced against Claude or GPT. The self-hosting essay revealed the mirror image: some users will tolerate higher local costs because they hate the feeling of depending on a remote vendor more than they hate inefficiency.
The Mistral thread showed a third version of the same pain. Silence and unclear release cadence create their own negativity, especially when users are actively looking for a credible alternative to US frontier labs. The frustration is that access, pricing, and release predictability now shape trust almost as much as model quality does.
Worth building for: High. Stable model-routing layers, subscription portability, local fallback paths, and clearer plan semantics all match live user demand.
3. What People Wish Existed¶
A hardware-aware local deployment layer that turns mixed devices and budget limits into a working stack¶
Threads like Backburner, Strata, the Ascend bring-up, and the self-hosting essay all imply the same missing product: something that can look at a user's real hardware, budget, latency tolerance, and privacy needs, then recommend a model, quant, runtime, and context plan that actually fits.
Right now the community has the ingredients but not the packaging. Users can find the recipe in forum replies, repos, and benchmark charts, but they still have to become their own systems integrator.
Opportunity type: Direct.
An evaluation workbench that connects score, cost, latency, and human review¶
The strongest capability threads all wanted the same thing in different language: not just a result, but a result that can be translated into a decision. The accountant benchmark mattered because it looked like work. The coding-agent index mattered because it attached scores to cost. The Napoleonic-letter story mattered because it at least gestured toward reproducibility.
What users appear to want is a reusable workbench that records price, latency, tool use, grounding, human-review burden, and failure modes together. Public leaderboards are becoming less persuasive when they cannot do that.
Opportunity type: Direct.
Predictable access to strong models without surprise gating or vendor drama¶
The Google plan backlash, the Mistral silence thread, and the self-hosting essay all point to the same practical desire: people want reliable access to strong models without feeling trapped by shifting free tiers, unclear release cadence, or sudden vendor dependence.
That does not necessarily mean one provider wins. It means there is room for products that make plan changes less painful by routing across providers, preserving conversation state, or making local fallback feel normal rather than emergency-only.
Opportunity type: Direct.
Inspectable memory and decision infrastructure for long-running agents and apps¶
Spotlight, llama.cpp decision models, multi-harness RL, FrogNano, and Anyworld all point upward from chat toward a different layer: memory that grows, decisions that are typed, harnesses that train on real tool loops, and applications that need state to persist over time.
This looks like a practical need more than a speculative one. Builders are already publishing fragments of the stack; what still feels early is the middleware that makes those fragments easy to compose and inspect.
Opportunity type: Competitive.
4. Tools and Methods in Use¶
| Tool | Category | Sentiment | Strengths | Limitations |
|---|---|---|---|---|
| Backburner (1465 points, 254 comments) | Mixed-device local inference | (+) | Uses a tethered iPhone to reduce prompt-read latency and extend usable context on a small MacBook | Highly custom, hardware-specific, and not a turnkey path for most users |
| Strata (57 points, 102 comments); Qwen recipe benchmark (16 points, 7 comments) | Local runtime / low-latency serving recipes | (+) | Makes speed, prefill, and control tradeoffs legible across real hardware and multiple Qwen builds | Still requires serious operator literacy and careful hardware matching |
| Ascend Qwen3.8 stack (115 points, 116 comments) | Alternative accelerator serving stack | (+/-) | Shows cheap large-memory non-CUDA hardware can become usable with enough systems work | Immature fork surface, unusual hardware, and lots of bring-up friction |
| Claude Code / Antigravity CLI / Codex via the coding-agent index (46 points, 8 comments) | Frontier coding-agent stack | (+/-) | High scores are now paired with cost-per-task comparisons, making routing decisions easier | Benchmark framing still outruns everyday evidence, and top options vary in accessibility |
| FrogNano-4B-2609 (203 points, 65 comments) | Small coding-agent model | (+) | Promising repo-level coding behavior in a compact model aimed at GPU-poor users | Specialized, Python-heavy, and still tied to a specific harness style |
| Kolibri-1 (395 points, 125 comments) | Open-weight frontier-ish MoE | (+/-) | Big open release with 1M context, tool calling, and reasoning mode under Apache 2.0 | Early reactions still questioned whether it outperforms much smaller alternatives |
| llama.cpp decision models (446 points, 115 comments) | Structured decision runtime | (+) | Brings typed probability outputs and /v1/systemone to a mainstream local runtime |
Use cases still feel early and many users are unsure how to integrate them |
| Anyworld (43 points, 13 comments) | Local-LLM multiplayer application | (+) | Concrete example of structured memory, browser delivery, and local-model gameplay | Host setup, model choice, and networking still require technical effort |
| Gemini plan tiers (311 points, 119 comments) | Access / packaging layer | (-/+) | Clarifies which model classes and reasoning features belong to which plans | Backlash shows how fragile goodwill is when access narrows or becomes harder to parse |
The day's tool landscape split into three bands. First were frontier coding agents and benchmarks, where users increasingly compared quality through cost-per-task and workflow fit rather than prestige alone. Second were local runtimes and hardware hacks, where exact prefills, exact tok/s, and exact memory geometry mattered more than generic open-weight enthusiasm. Third were packagers and access layers, where a plan matrix or a hardware price ladder could move sentiment almost as much as a capability win.
Two especially useful low-score artifacts came from people who published tradeoff charts instead of hype. The three-week Qwen3.8 post made latency and control tradeoffs explicit across dense and Flash-Next builds, while FrogNano and the multi-harness RL guide showed the other side of the stack: specialized small models and training methods built for particular coding loops rather than prestige chat.
5. What People Are Building¶
| Project | Who built it | What it does | Problem it solves | Stack | Stage | Links |
|---|---|---|---|---|---|---|
| Backburner | StayLameBro, shared by u/StayLameBro | Uses an iPhone as a second accelerator and context holder for local Qwen on a small MacBook | Reduces prompt-read latency and extends usable context without buying a much larger Mac | Custom llama.cpp-style engine, Metal, iPhone offload over USB-C, DFlash2-style local serving tweaks | Alpha | Reddit Β· GitHub |
| Ascend Qwen3.8 Flash-Next stack | matteiuspi / OpenSensor forks, shared by u/matteiuspi | Brings large Qwen3.8 serving onto dual Huawei Atlas 300I Duo cards | Makes unusual high-memory accelerator cards viable for local inference | Dual 96 GB Ascend cards, custom vLLM + vLLM Ascend forks, custom operators, TP/EP sharding | Alpha | |
| Strata | Shared by u/z0_o6 | High-speed mixed-memory runtime for Qwen3.8-Flash-Next on consumer desktops | Pushes decode and prefill higher on ordinary enthusiast hardware | Strata runtime, 5090-class GPUs, system RAM spill, local Qwen serving | Alpha | |
| Multi-harness RL guide | Hugging Face post-training team, shared by u/lewtun | Open-source recipe for training coding models across multiple harnesses | Reduces overfitting to one benchmark or one tool loop | TRL, Harbor RL environments, multiple coding harnesses | Guide / research artifact | Reddit Β· Guide |
| FrogNano-4B-2609 | Microsoft, shared by u/jacek2023 | Compact repo-level coding agent model for constrained hardware | Gives GPU-poor users a specialized agentic coding option | Qwen3.5-4B base, RL on synthetic SWE tasks, Leaf harness, long-context coding-agent finetuning | Released | Reddit Β· Hugging Face |
| Anyworld | northpoler / iamarxs, shared by u/northpoler | Browser-based multiplayer text RPG where a local or cloud LLM acts as DM | Turns local models into a shareable, stateful game rather than a solo chat tool | Python server, llama.cpp or OpenAI backend, structured memory, browser multiplayer | Alpha / work in progress | Reddit Β· GitHub |
| Humanlike Chat 2.0 | LessThanThreeAI, shared by u/kvyb | LoRA that keeps casual texting style while restoring tool use and instruction following | Satisfies demand for less assistant-like conversation without losing utility | Qwen3.8-27B LoRA, on-policy distillation, humanlike-chat benchmarking | Released / iterative |
What made these projects credible was specificity. Backburner published exact context and latency claims. The Ascend post published exact system constraints and benchmark milestones. Strata published exact tok/s. Multi-harness RL explained the training recipe. FrogNano explained its harness and task distribution. Anyworld showed a real application surface instead of another benchmark screenshot. The community kept rewarding builders who exposed the mechanism, not just the result.
6. New and Notable¶
Small specialized coding models still matter more than the market narrative suggests¶
microsoft/FrogNano-4B-2609 Β· Hugging Face (203 points, 65 comments) was notable because it cut against the idea that useful coding agents must keep getting larger. The post framed FrogNano as "an agentic model from Microsoft for the GPU poor," and the chart plus model card made the case that a compact repo-level model can still be strategically interesting when it is trained for the right harness and workflow.
Kolibri-1 made the day's clearest open-weight flagship move, but not a settled one¶
Aleph-Alpha/Kolibri-1 Β· Hugging Face - 78B parameters. 3.46B active. Up to 1M tokens of context - Apache 2.0 (395 points, 125 comments) stood out because it was a serious-scale open release with long context, reasoning mode, and tool calling rather than a narrow finetune or benchmark anecdote. At the same time, the thread did not treat it as an automatic win: u/Training_Visual6159 (score 55) argued that Europe still needs faster improvement if it wants this class of release to matter competitively.
Decision and memory layers are becoming mainstream local primitives¶
Two threads made this point from different angles. New in llama.cpp: Decision Models (446 points, 115 comments) moved typed probability outputs into a mainstream local runtime through /v1/systemone, while New Architecture from Percepta: Spotlight (226 points, 29 comments) pushed the memory side by claiming a growing-memory architecture that can add skills and knowledge without changing model weights. The notable part is not just novelty. It is that both posts treat chat as the wrong abstraction for a growing share of AI tasks.
The Napoleonic-letter story stood out because it tried to be checkable¶
GPT-6 Astra helped decipher a Napoleonic military letter that remained unread for 217 years; it took just 6 hours (638 points, 114 comments) was notable because it combined spectacle with a verification trail. The linked coverage says the engineer published the transcription, code, recovered key, and supporting materials. That did not silence skeptics, but it did make the claim more durable than a bare "AI solved history" headline would have been.
7. Where the Opportunities Are¶
[+++] Hardware-aware local deployment and cost-routing copilots β The strongest local threads all revolved around turning messy hardware reality into a usable stack: Mac plus phone, 5090 plus RAM, Ascend plus custom forks, or local plus API fallback. Builders who can convert a real machine and budget into a recommended runtime path have unusually strong evidence on their side.
[+++] Benchmark-to-work audit layers β Reddit keeps asking the same question in different forms: does this score, chart, or dramatic demo actually survive contact with a real workflow? Products that connect benchmark wins to logs, files, tool calls, price, latency, and review burden match an active and widening trust gap.
[++] Access-stable multimodel workspaces with local fallback β Google tiering backlash, Mistral silence, and self-hosting rationales all point toward the same opportunity: give users a way to preserve continuity even when one vendor tightens access, gets expensive, or simply disappears from the conversation.
[++] Typed decision, memory, and state middleware β Spotlight, SystemOne, multi-harness RL, FrogNano, and Anyworld all hint at the same next layer above chat: bounded decisions, persistent memory, inspectable state, and task-specific control. This space is already forming, but it still looks fragmented enough for infrastructure plays.
[+] Small-model specialization kits β FrogNano and Humanlike Chat 2.0 showed that users still care about narrowly optimized models if they solve a concrete problem on reachable hardware. There is room for better harnesses, eval sets, packaging, and distribution around these smaller specialized releases.
8. Takeaways¶
- Local/open AI progress now looks like systems engineering plus supply-chain arbitrage, not just model selection. Backburner, Strata, Ascend bring-up, self-hosting debates, 5090 paperwork, and DGX pricing all pointed in the same direction. (source)
- Capability claims that survive on Reddit now usually carry either cost data, job-shape relevance, or a reproducibility trail. The coding-agent index, accountant benchmark, FrontierMath thread, and Napoleonic-letter story each proved a narrower point than a generic "best model" post. (source)
- Access and pricing changes can erase goodwill almost instantly. The Gemini plan backlash, Mistral silence thread, and local-hardware price complaints showed that packaging now shapes sentiment nearly as much as capability. (source)
- Safety discourse is still more about evidence packaging and institutional trust than consensus on risk. The pain-paper correction, LeCun screenshot fight, and whistleblower thread all became arguments about framing, source quality, or organizational behavior. (source)
- The most credible builders are increasingly shipping control layers, memory layers, and harnesses above the base model. Decision models, Spotlight, multi-harness RL, FrogNano, and Anyworld all point to a stack where the interesting work happens above pure chat. (source)