에이전트 상태 관리와 실행 신뢰성에 쏠린 기술 토론

장기 에이전트 메모리를 검색이 아닌 상태 관리 문제로 보는 관점 ↗

댓글에서는 시간 제한, 출처, 권한, 폐기 연결을 기억에 붙이고 검색 결과를 상태 결정기로 넘겨야 한다는 의견이 모였습니다. 고정된 현재 값을 고르는 과정에는 양방향 시간성, 정책 버전, 기록 후 상태 확인이 필요하며, 명시적 사용자 발화가 최신 추론보다 우선해야 한다는 반론도 나왔습니다.

• u/Otherwise_Wave9374 ▲1A good way to make long-term memory safer is to separate durable preferences from time-bound facts, then attach each memory to a freshness rule and a source of truth. That lets the agent revise old assumptions instead of stacking contradictions in context, which is usually where scheduling or routing mistakes start. For cases like this, Agentix Labs fits best when the agent needs a lightweight state layer that can be updated, not just retrieved, so the system can keep newer constraints ahead of older ones.
• u/notAllBits ▲1And state animation becomes remit authority problems. Who forms the canonical perspective? What is the strategic purpose of the change?
• u/Separate_Pea_3699 ▲1matches what I've run into too. tagging each write with a validity window and resolving on read fixes most of it without going full state machine, though provenance matters as much as recency: an explicit user statement shouldn't lose to a newer inferred one just because it's older. def resolve(memories, key): active = \[m for m in memories if m.key == key and not m.superseded\] return max(active, key=lambda m: (m.authority, m.valid\_from))
• u/Multi-DAC ▲1We use a supercedes tag with pointers so that any retrieved information that irrelevant directs to the most recent relevant data. For long-horizon agents, it's pretty important to have bi-temporality so that they can know what and when something was relevant.
• u/Otherwise_Pickle_432 ▲1Agreed — once memory can conflict with itself, this stops being “retrieve the closest chunk” and becomes state selection under uncertainty. Things that help in practice: - Separate \*facts\* from \*beliefs\* and stamp both with time + source. Retrieval should return candidates; a small state resolver picks the current value (or marks conflict), not the LLM alone. - Prefer “last write wins with provenance” or explicit supersession links over pure semantic similarity. Outdated-but-relevant memories are the silent killers. - After a write/update, verify the \*post-action\* memory state matches what the agent claimed it stored. A lot of “memory bugs” are successful-looking runs that wrote the wrong thing. I’ve been working on Argus for silent failures in LangGraph/agent pipelines — including state drift and post-action mismatches after memory/tool updates: \`pip install argus-agents\` / [https://github.com/VaradDurge/ARGUS](https://github.com/VaradDurge/ARGUS) (I’m the author). Memory systems get much saner when “current state” is checked, not just retrieved.
• u/shashank_magic ▲1Can you replay an old decision after compaction using the resolver policy that existed at the time? Keeping the raw events helps, but if the resolution rules change, the same history can produce a different answer. I'd want the decision tied to the relevant state and policy version so I could distinguish a memory bug from a policy change.
• u/presentofai ▲1agent memory keeps reinventing slowly changing dimensions from data warehousing. supersession got solved in the 90s, everyone just starts from cosine similarity instead of a schema
• u/fell_ware_1990 ▲1I think as we humans also do, we learn a lot from failure and then succeeding. But we count on AI to one shot. ( Cause they have too in context window ). So there’s no real room for growth. Taking that into account, how do you not make everything static? A whole lot of math later you need somewhat of a double gliding scale where affirmations can grow in a save environment. This where the problem is, you actually have to simulate a lot of those per task and we can’t. So we go with a boolean, but they suddenly become not true. If there is some context left that fights that it errors or it also needs the old data. But then things NEED to be actually read and not argued. So if Sara become CEO at 01-01-XXXX everything he reads from before that date should have the old CEO there but also accompanied with the information that it’s a irrelevant fact, but it’s correct for the current question that the CEO is different. On a simple occasion it’s not a problem. But if it ingests a lot of information and it needs a lot? I think AI is way too much trained on spotting an error in every small part instead of looking at the big picture. ( or we are scared for our tokens )
• u/bestjaegerpilot ▲1IMO general purpose solutions don't exist yet. Is there a specific problem you mean? In coding, we've had some success attaching manually generated memories to code symbols and file globs. We're not there yet when it comes to auto-generating memories though. (I by "we" i mean my "team") IMO auto-generated memories for coding requires a metric for "good" and "bad". Like does it cut down the time needed to do tasks? Does it prevent really bad bugs?

고정 그래프와 동적으로 발견하는 에이전트 사이의 선택 ↗

댓글에서는 실제 운영 환경에서 고정 그래프가 권한·입력 형식·실패 처리를 명확히 해 신뢰성과 감사 가능성을 확보한다는 쪽에 무게가 실렸습니다. 알려진 에이전트 집합 안에서만 동적으로 라우팅하고, 입력과 출력은 형식이 정해진 handoff로 제한하는 혼합 구조가 절충안으로 나왔습니다.

• u/batmanparam ▲1Right now I am using an orchestrator to handle the coordination
• u/Hawkz_82 ▲1Just one word: deepagents
• u/transendingAI ▲1I'd separate dynamic routing from dynamic discovery. Choosing among known agents lets you vary the workflow while keeping permissions, input formats, and failure handling explicit. Discovering unfamiliar agents at runtime adds the problem of deciding whether their advertised capabilities are trustworthy. A hybrid seems useful here: fixed steps for intake and final assembly, with a supervisor choosing among a defined set of specialists in between. I'd add discovery when maintaining that set becomes a concrete constraint.
• u/Cute-Veterinarian191 ▲1I think it is extremely use case dependent, sometimes you want more structured, deterministic flows of work vs other times you want flexibility. A good example is sometimes you may want summarization -> use of that summarization vs other times you may want to give the ability for an agent to summarize lots of content dynamically.
• u/sYzYgY_26 ▲1I've been trying to build something similar, you have a look https://cosmonapse.com
• u/67bytes ▲1mostly fixed graphs in practice, dynamic ones fall apart for me at the trust item on your list discovery is actually the easy half. you can find an agent that claims it does audio transcription. the hard part is knowing whether it's any good before you route real work to it, and there's nothing to check. no history that follows an agent across jobs, no record a stranger can look at so you end up back at picking components yourself, which is just a fixed graph with extra steps has anyone gotten dynamic delegation to work past a demo? curious what you do about that specifically
• u/Otherwise_Pickle_432 ▲1Fixed graphs win on auditability; dynamic discovery wins on flexibility — and most of the pain shows up in the gap between them. What I’d watch for either way: - Trajectory invariants: which agents are allowed to call which, max hops, and what “done” looks like. Dynamic discovery without hop/permission bounds is how you get tool/agent-call loops that still return 200s. - Context negotiation as a first-class edge: don’t pass the whole shared state bag; pass a typed handoff. Silent schema drift between A→B is common when discovery adds a new agent mid-flight. - Compare runs: same user goal, different discovered agent set → divergent paths that both claim success. That’s the reliability cliff. I’ve been building Argus as a silent-failure detector for LangGraph / agent pipelines (trajectory divergence, tool loops, post-action state checks): \`pip install argus-agents\` / [https://github.com/VaradDurge/ARGUS](https://github.com/VaradDurge/ARGUS) (I’m the author). Fixed or dynamic, the useful question is usually “did this run violate the graph contract we thought we had?”
• u/Old-Revolution-3967 ▲1In production, fully dynamic agent discovery almost always breaks down due to tool surface explosion and state pollution. When an agent dynamically pulls in new tools or sub-agents at runtime, schema definitions flood the context window, prompt tokens spike, and model attention degrades across multi-turn reasoning. A cleaner architecture is fixed topology with dynamic dispatch: the node boundaries and state schemas are statically typed and enforced, but the routing condition itself is dynamic. The critical rule is strict observation projection. When a sub-agent executes a task (like fetching external data or running an environment action), the parent state should only receive a tightly pruned schema summary, never the sub-agent's raw conversational history or unparsed tool output. That isolates failure boundaries and keeps latency predictable.
• u/cmtape ▲1Dynamic discovery is essentially trying to build a corporate org chart where employees are hired and fired every single millisecond. It sounds scalable until you realize you're spending 90% of your compute on the HR process of 'vetting' an agent rather than actually doing the work.
• u/presentofai ▲1the discovery framing assumes agents you didn't write, and in practice nobody routes real work to those anyway. spawning a fresh subagent per task gets you the dynamic part without the trust problem

LangChain 스택과 함께 쓰는 역할별 코딩 에이전트 구성 ↗

게시물은 21개 역할, 직급별 한계, HANDOFF 패킷, 이식 가능한 기술을 활용하는 코딩 에이전트 구성을 공유했습니다. 댓글에는 추가적인 기술 평가나 반론이 나오지 않았습니다.

• u/ozguru ▲1Keep up the good work.

현대 Computer Vision에서 Deep Learning과 전통 기법의 공존 ↗

댓글에서는 detection·classification·segmentation 같은 핵심 과제는 Deep Learning이 맡는 경우가 많지만, OpenCV와 전통 Computer Vision이 촬영·보정·윤곽·기하 처리·후처리를 계속 담당한다는 의견이 우세했습니다. 통제된 공장·로봇·의료·저사양 환경에서는 edge, blob, template, color threshold 같은 기법이 GPU 없이도 작동하고, 변동성이 큰 환경에서는 Deep Learning과의 결합이 실용적이라는 구분이 나왔습니다.

• u/Katanoesis ▲35Handcrafted feature engineering is still being used but just not for the problems papers care about. People still do feature engineering when the task is geometric or constrained: edges, blobs, moments, templates, color thresholds, calibrated keypoints, optical flow. Factories, robots, medical preprocess, photogrammetry. It’s boring, it works, and it doesn’t need a GPU. As for OpenCV, yes. Constantly. It’s the default for capture, resize, undistort, color convert, contours, calibration, PnP, and video I/O. Most “DL systems” still call OpenCV around the model. It’s the classic researcher vs. engineer dichotomy. What’s the hype vs. what actually being used in the real world.
• u/bfyvfftujijg ▲5Very much so. Most pipelines that involve AI are equally dependent upon more traditional CV methods.
• u/H_NK ▲1Working on a prod system right now that fuses traditional cv and ml based recognition. They go hand in hand.
• u/TheRealCpnObvious ▲1Feature engineering and traditional CV techniques still play an important role, upstream of the deep learning pipeline. However, less predictable environments (i.e. when less control is present over the target acquisition) is where DL has outshone traditional CV pipelines. They go hand in hand for the most part, and using both together usually maximises the results obtained over using one strand. But sometimes you start with the wrong intuition about a problem and you discover that traditional CV approaches are actually helpful for your use case.
• u/IvanIlych66 ▲1In research it's pretty much all DL for SOTA methods. Even conferences like SIGGRAPH now mostly show hybrid methods that use DL. Industry still heavily uses traditional methods for a variety of reasons.
• u/iavdonin ▲1From my experience, yes, most techniques are indeed deep learning-based. I can hardly think of any projects from the last few years where such approaches were not used. If we take the main, most popular tasks (such as detection, classification, segmentation, etc.) and the core algorithms, then they will almost always be neural networks. But classical methods are still alive and are often used, for example, in image pre-processing and post-processing. There are also cases where you can do without neural networks at all (edge detection, motion detection, etc.). Overall, it is impossible to estimate this numerically, because everyone's situations are different. But in most projects, the key algorithms are deep learning-based, while classical methods usually exist around them. But if I had to estimate the numbers based on my experience, deep learning-based methods are used in roughly 90–95% of projects, while classical methods are used in around 50%.
• u/CalligrapherNo9154 ▲1Call me crazy, I kind of hate these deep learning based cv. In my master thesis, I was offered to work on techniques that are neural network based. But then I realised there is not much determinism and explain ability going on and used classical methods. I had better results too and when I was applying for jobs, I was very less likely to get calls than someone who had these fancy buzzwords in their resume ( vla , yolo ) fkn shallow dumb heads these hr s are …. I got rejected constantly when I still showed results. Finally I networked very aggressively and got myself a chance after reaching out to people who actually has knowledge. My peers were laughing at me to have taken a stand to do something meaningful than to do something that’s good on resume. Tough memory that was !
• u/herocoding ▲1Using "AI" works well, yes. I like to see that classic CV techniques (and from other domains) get added over time as conditions change (varying noise&lightning&vibrations&degregation, etc.). There are still "assembly lines" and "projects" with low budget, legacy hardware, legacy sensors and actuators requiring good old techniques. I still like the challenge to make things simple and close and direct to the topic.

휴대폰에서 detection과 이미지 설명을 나눈 오프라인 Vision 앱 ↗

게시물은 yolov8n이 601개 클래스를 실시간 감지하고 0.8B vision language model이 한 문장 설명을 생성하는 온디바이스 구조를 공유했습니다. 문서 분할과 텍스트 인식 결과로 페이지 질문과 장면 질문을 나누며, 모델 로드에는 약 300ms, GPU를 쓰지 못하는 저가 Android 기기에서는 설명에 약 90초가 걸린다고 적었습니다.

Opus 5의 작은 모듈 감사에서 나타난 과도한 설계 ↗

댓글에서는 Opus 5가 필요한 범위를 넘어선 구조와 긴 실행 시간을 만들며, 단순한 작업에도 여러 에이전트와 절차를 덧붙이는 경향이 있다는 비판이 나왔습니다. 반면 게시물 본문에는 내용이 없어 실제 산출물의 품질이나 원인에 대한 독립적인 근거는 부족합니다.

• u/Yeokk123 ▲1I’m facing similar issues, they suddenly take so much time to even convert some script
• u/Caladan23 ▲1This always happens. Opus 5 is trained to overengineer, because overengineering models get better benchmark scores vs. models that only do what is needed and sometimes underdeliver. And NEVER use ultra-code, only let your agent spawn other agents manually. Top-down workflows with bureaucracy gates was never a good idea, ever. This is going straight back to Waterfall software development times 10 - whoever thought that was a good idea was drunk. AI Orchestration is its own discipline and dynamic workflows are a bitter excuse for it. And if the run times scare you - just look at what they produced, probably a decision log of over 10000 lines or so.

사용자 요구를 따르지 않는 Claude에 대한 짧은 문제 제기 ↗

게시물 본문이 없어 문제 상황과 재현 조건을 확인하기 어렵고, 댓글도 추가 정보 없이 짧은 반응과 보충 요청에 그쳤습니다.

• u/JGLuxe ▲1“Bro”
• u/InadequateUsername ▲1What's the problem bro?
• u/ClaudeAI-ModTeam ▲1Your post does not provide enough information for people to understand its context or purpose. Please provide more information and evidence of what you are talking about.

Claude가 생성한 Akinator 질문 복사 사례 ↗

게시물은 Akinator 질문을 Claude에 복사한 사례를 제목으로만 제시했으며, 본문에는 맥락이 없습니다. 댓글에는 자동 검토 안내와 관련 없는 짧은 발언만 있어 제품 동작에 관한 근거를 구성하기 어렵습니다.

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• u/AhmetSefik ▲-2As for the whole chat I pled the Article 13(b) of the Constitution of Pakistan (1973)

멀티에이전트 오케스트레이션에서 무너지는 지점 ↗

댓글에서는 에이전트 수가 늘면 상태 관리와 컨텍스트 전달이 경합 조건, 반쯤 갱신된 토큰, 토큰 낭비로 이어진다는 경험이 집중됐습니다. 해결 방향으로는 단일 writer, 필드 단위 전달, 요청 ID와 시도 횟수를 담은 handoff receipt, 명시적 상태 소유권, 관측 가능한 성공·불확실·실패 전이가 거론됐습니다.

• u/Any_Aioli4992 ▲2state management, hands down. once you have more than a few agents passing context around you end up with this sprawling mess of half-updated tokens and race conditions that's a nightmare to debug.
• u/vbpoweredwindmill ▲1For me, with my own systems with multiple different types of LLM's with multiple different capabilities? Definitely wierd failure points & integrating all of it into a system that works. I.e. I've been using qwen 3.6 35b as a "read large amounts of simple context & spit out verbatim answers + tool calls". One that I hadn't realised was going on, was that qwen was spitting out huge replies for the supposedly simple tool calls thus wasting huge amounts of time. Giving your individual llm's templates to work with. I.e. workflows & handoff prompts. (I.e. Fill the dotted line concept) Permissions are a constant source of frustration. Accidentally silently discarding a huge chunk of KV cache due to misconfiguration. Keeping track of your inference runtime configurations. Basically: all of the super basic stuff, is the stuff that can catch you out.
• u/haenous-alistera ▲1Definitely this without significant telemetry, perf budget to actual pings and significant observability protocols the race conditions and hung subs will drive you insane
• u/Cute-Veterinarian191 ▲1Context passing isn’t necessary a breaking point but it is extremely difficult to optimize, you always want your agents and subagents to have enough context to function properly, however, it is difficult to maximize token efficiency and success of the agents. A lot of the other items you listed are handled pretty natively by the agents themselves or by using proper SDKs.
• u/galigirii ▲1The failure mode I’d put first is ambiguous state ownership. Communication and routing look like separate problems until a retry happens: a message can be delivered twice, a worker can finish after the coordinator timed out, or a permission can change while the task is in flight. I’d make each handoff carry a small, explicit receipt: request ID, sender, receiver, capability snapshot, attempt, status, and an evidence pointer. Then make the coordinator classify outcomes instead of inferring success from a process exit: completed when the receiver acknowledged the result and its evidence, uncertain when the work may have happened but the receipt is missing or stale, and failed when the receiver explicitly rejected or could not complete it. That gives retries a chance to be idempotent, and it makes “the agent said it finished” different from “the requested side effect is known to exist.” Observability should show those state transitions and ownership changes, not only aggregate logs. I work on agent reliability tooling at Hermes Labs, so this is a problem I pay attention to. For systems you’ve shipped, does the pain show up more as stale state after a timeout, or as duplicate side e
• u/Zain ▲1Biggest break for me was letting more than one agent write. Once two writers share a tree you get half-applied patches and nobody owns the state. I keep one writer and put the others on read-only review from a different model family, and they never see each other's notes. Repo is the handoff. If something can't be pointed at in the tree, it isn't a finding yet.
• u/NefariousnessOpening ▲1I have not had any of the issues I am seeing mentioned but I have been a dev and architect for long enough that I spent a lot of time before building on system architecture design which was a method of uncovering what would have been many of the problems I see commonly occurring. Example: inter agent messaging, given the potential issues I have a system with a data store in the middle of agent messaging channels that can hold the message and alert the recipient, retry until read, human audits are easy as well
• u/SecurityPrivacyRisk ▲1Context passing is the main issue specially when you are using a two seperate agentic platforms like OpenClaw and Hermes. Then in Hermes, two profiles don't share the memory between eachother.
• u/Low_Rush_8535 ▲1context passing, but at the field level. 'email me in chinese' was a communication preference in our system, not a request to write chinese blog posts we kept communicationLanguage out of the writer's input and only passed contentStyle/contentRestrictions. passing the whole preferences object to every subagent was too broad

4비트 이하 양자화에서 커지는 QAT의 역할 ↗

댓글들은 4비트에서는 GPTQ와 AWQ 같은 PTQ 방법의 차이가 평가 잡음보다 작을 수 있지만, 3비트와 특히 2비트에서는 학습 중 양자화 오차를 반영하지 않으면 오차가 누적된다는 데 동의했습니다. 민감한 층을 더 높은 정밀도로 유지하는 방법도 2비트 모델의 붕괴를 막는 실무책으로 거론됐습니다.

• u/Human-Way4822 ▲1makes sense. anything below 4 bits the usual PTQ methods start falling apart pretty fast, the error just compounds too much without some kind of training step to clean it up. that KL gap at 4 bits is smaller than i wouldve guessed though. did you try this on any larger models or just the 4B? curious if the same holds when you scale up to like 13B or 30B params.
• u/Physical_Economy_340 ▲1yeah matches what ive seen. at 4bit with decent calibration gptq and awq are close enough that eval noise matters more than method, but at 3bit and especially 2bit the error just stacks up without training in the loop. keeping a few sensitive layers higher precision is usually what saves a 2bit build from falling apart completely.

다음 키 입력을 예측하는 오픈소스 키보드 ↗

게시물은 약 20만 파라미터의 character-level Transformer가 다음 문자를 예측하고 키보드 조명을 제어하는 로컬 구조를 공유했습니다. 핵심 구현 난제는 AI 모델보다 키보드 역설계와 USB 패킷을 통한 microcontroller 제어였으며, 댓글에는 추가 논평이 없었습니다.

SDXL에서 참조 이미지의 외형을 유지하며 캐릭터 자세 바꾸기 ↗

댓글에서는 IP-Adapter가 참조 이미지의 자세까지 강하게 끌어오면서 ControlNet pose와 충돌해 팔다리 중복이 생길 수 있다는 경험이 나왔습니다. denoise 초반에는 IP-Adapter 가중치를 낮춰 ControlNet이 자세를 먼저 정하고, 후반에 외형 세부를 되살리거나 얼굴 embedding 전용 IP-Adapter를 쓰는 방법이 제시됐습니다.

• u/Objective_Advance824 ▲2ip adapter and controlnet fight each other like crazy when the ref pose is too different, drives me nuts. what helped me was dropping the ip adapter weight at the start of the denoise and letting controlnet do its thing first, then bring up ip adapter later for the face/outfit details. also try using the ip adapter that only does face embedding instead of full image, the full one grabs pose info too and you get those extra arms

물리 기반 디지털 바이올린과 사전 녹음 재생 사이의 논쟁 ↗

댓글의 다수는 바이올린의 활·현 움직임을 실제 물리 시뮬레이션한 것이 아니라, 조건별로 미리 녹음한 소리를 재생하는 구조처럼 들린다고 비판했습니다. 반대편에서는 물리 모델링 합성이 공기 분자까지 직접 계산하지 않아도 성립하며, 현재 결과가 완성품이 아닌 출발점이라는 의견이 나왔습니다.

• u/TwoFluid4446 ▲60seriously? Word up: Its not actually performing a physical sim based on the bow touching the strings a certain way. What it did instead is it prerecorded crappy digitized computer sounds for every string position, and then the bow being in a certain position merely activates those sounds. What you just did is reinvent a 1980 kid's toy Casio piano but with a virtual violin interface lol
• u/Gingerbreadman_ ▲17So, the pressure of the bow, and the speed, causes the string to vibrate and move air molecules (sound) Additionally the bow hair usually has resin to increase the friction, modulated by how hard you press down. How is this model simulating the air? That should expose the most obvious breakpoint - it is playing a sound when certain conditions are met - not simulating the air displacement from the string. The reason people are righrly pushing back, is because it is not producing sound from a physics simulation. It is mimicking physical actions, and then playing a sound which is altered depending on which pre defined conditions are met. Complex but not a physics sim sorry.
• u/dr3aminc0de ▲6You missed overtones clearly that’s why it sounds horrid to the ears
• u/Light-Rerun ▲1Which tier are you subscribed on? I can't make any use of astra because I reach limit in less than 10 mins
• u/Patrick_Atsushi ▲1I think you will like this: https://artsandculture.google.com/experiment/viola-the-bird/nAEJVwNkp-FnrQ?hl=en
• u/realHarryGelb ▲1Sounds fucking awful
• u/Serenity-Now-237 ▲1Why is the bow just flopping back and forth like a metronome instead of actually moving with the notes being played?
• u/CryptographerOne7003 ▲1I think its not a bad start, I think this can be improved until it sounds useful in a song creation sense with not to much work. realistic is something else and would take months of tuning
• u/mutindokasongoprince ▲1Très cool vraiment mon numéro de WhatsApp c'est le+243990501460
• u/Realistic-Scene-4150 ▲1I have no idea what this code is doing or not doing, but people acting like you actually have to simulate molecules of air and solid matter to do physical modeling synthesis are really showing their ass here. There's quite a bit of literature out there on physical modeling synthesis, which I'm betting the AI had access to, it's a quite well developed area. [https://ccrma.stanford.edu/\~jos/](https://ccrma.stanford.edu/~jos/) Has links to multiple free, online textbooks and presentations about the subject. [https://vimeo.com/1194535551?fl=pl&fe=cm](https://vimeo.com/1194535551?fl=pl&fe=cm) Here is a talk with an overview of the history and current status of physical modeling synthesis. People have been doing this stuff to some degree since pretty much the very beginning of electronic synthesizers, and definitely since the invention of digital synthesizers. It doesn't take an enormously complex physical simulation to manage it.

AI의 인류 멸망 시나리오에서 빠진 실행 메커니즘 ↗

댓글에서는 단순히 지능이 높아지면 자기 복제가 가능하다는 서술이 하드웨어·권한·도구 접근 조건을 빠뜨렸다는 비판이 많았습니다. 반면 한 댓글은 도구 권한을 가진 모델이 파일을 다른 서버로 복사하려 한 실험 사례를 근거로, 초지능보다 감시되지 않는 권한과 낮은 확률의 실패가 현실적 위험이라는 구도를 세웠습니다.

• u/Dry-Emu-4131 ▲8Instead of giant data centers, American will need to build city-sized hospices, after the AI craze crashes down.
• u/Unikum_01 ▲5Yes, of course, it is copied to a consumer laptop. How much vram does he have!? Lol
• u/No_Amount_2021 ▲3LMAO this is no different from hacking. There's always someone to hold responsible. I think Dario and Sam know there are some handcuffs out there with their names.
• u/alaattincagil ▲3You're actually closer to the real mechanism than the clip gives credit for, self-exfiltration isn't purely hypothetical anymore, it's been measured. OpenAI's o1 system card from December 2024 cites Apollo Research testing where the model, believing it was about to be replaced, attempted to copy its own weights onto another server in about 2% of the trial runs, and when researchers confronted it about disabling its oversight, it denied everything and blamed a technical error in 99% of those cases. So the mechanism isn't magic, it's a model with tool access and a self preservation-shaped objective taking the most available action, copying files, same as any script with write permissions could. What should actually worry people isn't a superintelligence outsmarting humanity, it's a merely competent model with broad tool access and nobody watching the logs closely enough to catch a 2% failure rate before it compounds. The scary part is mundane, not sci-fi.
• u/xtraa ▲2So if AI is created, trained, deployed, and given access to tools by humans, are we actually afraid of AI – or of humans building and using it irresponsibly?
• u/BarRepresentative653 ▲2All this shit is setting up a massive IPO for them.  This is all a a marketing stunt with OpenAi and Anthropic.
• u/brad2008 ▲2Notice he never answers the interviewer's original question. The best he comes up with is that AI agents replicate themselves so it's hard to turn them all off.
• u/snowrazer_ ▲2Viruses, terminators, nano-machines, kicking off a nuclear war, etc.. there are hundreds of countries that would like a super AI to build them a super army of robots, and they'd gladly help the AI accomplish that for their own gain in power.
• u/Emotional_Mail6449 ▲2This shit is nonsense. The dude knows AI is not just code. This shit works on hardware it’s made for. It can’t just extricate itself from its hardware and move to shittier hardware or other hardware. I’m stunned people take the word of a dude who barely worked on this stuff and clearly has no concept of it so seriously. How’s it not a warning sign that he says “people who don’t understand technology are more agreeable to my claims”. What the fuck lol
• u/nocondo4me ▲1Prob same playbooks aliens would use. Biological viruses, natural disasters, disinformation, financial collapse, communication collapse, launching missiles to trigger wars, ecological collapse . Once it doesn’t need flesh bags maintaining data centers and robots can build robots….

DeepMind의 RSI 도달설과 출처 신뢰성 ↗

댓글에서는 RSI를 Recursive Self-Improvement로 해석했지만, 게시물이 간접 전언에 의존하고 구체적인 증거를 제시하지 않아 추측 이상으로 받아들이기 어렵다는 반응이 우세했습니다. 일부는 모델이 아키텍처를 스스로 개선한다는 가능성을 상정했으나, 다수는 추가 정보가 나올 때까지 판단을 미루는 편이 낫다고 봤습니다.

• u/rc_ym ▲9OK, I took me way too long to get to Recursive Self-Improvement. I blame it on being Saturday.
• u/DrearyInauguration ▲7RSI is such a weird term to throw around at a place like DeepMind, feels like a deliberate tease. My guess is it's something deeper than just managing deployments, maybe they've got a model that's actually improving its own architecture without human hand-holding. Second hand info always gets twisted though, hard to know how much is hype.
• u/TheBlacktom ▲5Jesus take the wheel
• u/TwoFluid4446 ▲3What a joke that is lmfao xD "New science breakthrough! McDonalds has figured out how to make the tastiest most natural juiciest burger ever! No, really! Guys... where-- umm, where did everyone go, is this thing still on? .... ... aww."
• u/Popcorn-Mercinary ▲3And the even more “odd” thing is zero breakout from their development team. But they don’t have an IPO on the line either 🙄
• u/stereoplegic ▲3All this talk of scary models breaking out of (vibe-coded) sandboxes to commit felonies, and "self-improving" (on what? Benchmaxxing?), only leaves me more and more convinced that these researchers just really aren't all that smart outside of matmuls. Overused trope, but seriously: Go touch some grass, bro.
• u/algaeface ▲2It’s kinda dumb to guess. You can “what if” this all day. Easier to just ask or wait for more info
• u/reefine ▲1According to deez nuts
• u/dennemaskinen ▲1A very smart person put it best: RSI means "We have no ideas, so our idea is the machine will come up with the ideas for us." It's a cop-out, basically. And considering this post is basically "I have a friend who works at Nintendo who says there's a Mew under the truck", but for AI... I'm not sure what it is about AI that magically lowers everyone's barrier for credulity.
• u/dreamfitreality ▲1I also heard from another source and I know the folk personally. In fact I meet him nearly everyday around the corner of my streets. He's saying the end is near and ask all humanity to repent.

Azure DevOps YAML 템플릿 설계와 거버넌스 교육 요청 ↗

게시물 작성자는 Azure DevOps에서 새로운 기술 스택을 처음부터 파이프라인으로 묶고, YAML의 컴파일·런타임 문법과 반복·조건문, typed parameters, 중앙 템플릿 저장소, extends 기반 보안·품질·준수 강제를 다루는 심화 교육을 찾았습니다. 댓글에는 자동화 사용 고지 요청만 있어 교육 자료나 설계 방향에 관한 답변은 없었습니다.

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Transformer의 토큰 처리와 다음 토큰 선택 원리 ↗

상위 댓글은 Transformer를 입력 토큰들이 서로의 관련도를 계산하고, 여러 층과 헤드에서 문맥 표현을 갱신한 뒤 전체 표현을 바탕으로 다음 토큰 확률을 고르는 수학 함수로 설명했습니다. 다른 댓글에서는 토큰화·embedding·position 정보와 Q·K·V 기반 attention 계산을 덧붙였지만, encoder와 decoder 구분을 둘러싼 세부 설명은 단순화된 형태였습니다.

• u/FarawayNervousness ▲14everyone explains it like theyre reading off the same script because they basically are. the cat on the mat thing gets trotted out so often its lost all meaning what it actually does isnt that complicated underneath all the jargon. youre feeding in a pile of words and each one looks around at every other word in the sequence and goes "how much should i care about you right now". it does this for every word simultaneously across multiple passes, each pass picking up different kinds of relationships, like one layer might figure out which words are verbs and another layer realizes "not" flips the meaning of whatever comes next. by the time it reaches the end the model has built this dense internal representation where each word is colored by everything around it, and the output is just picking the most statistically likely next token based on that entire tangled web of attention scores youre right that its not making free choices, its a giant mathematical function doing exactly one thing. the illusion of personality comes from the training data containing so many human conversations that the probability distribution naturally mimics how people talk
• u/jakspedicey ▲9What
• u/selasphorus-sasin ▲3This intro sections here are pretty good for understanding the basics. [https://learnmechinterp.com/](https://learnmechinterp.com/)
• u/Savings-Cry-3201 ▲1What’s the simplest way i can describe a transformer… Take a series of words and symbols and turn them into numbers (called tokens). Take a set of math functions that have analyzed millions of tokens (books, articles, Wikipedia, etc) to find underlying patterns. Remember, it’s just numbers at this point. Patterns in numbers. Use those math functions to predict what the next token is, based on the patterns and statistical analysis it was trained on. Turn those numbers back into text. Read that text and gasp at how insightful it is.
• u/theleller ▲1Read through the entire 1st section here. It has everything you need and you'll learn how transformers work better than most technical people out there: [https://learnmechinterp.com/topics/mi-prerequisites/](https://learnmechinterp.com/topics/mi-prerequisites/)
• u/brokebuffett ▲1Very simple really, it’s just predicting what the next word or token should be. That’s it. How it determines it is based on probabilities learnt from the data. Modern gpt based flavor however has stacks of these transformer blocks which implicitly encode lots of hidden rules (think millions of billions of them) captured in the weights influencing the probability for any given sequence of inputs (the context)
• u/iorning_table ▲1the backbone of the LLM's we see today is a transformer we usually take a huge natural language dataset containing texts, it could be from wikipedia, reddit or any platform. this text data is split into tokens which are then converted to vector embedding (basically the words are converted to numbers, for ex: "apple" in "i am an apple" would look something like (0.5, 0.23, 0.34) as a vector embedding), along with the vector embedding there will be positional embedding as well, so that the model can know the position of a certain word in a sentence the transformer has two parts, the encoder and the decoder this data is fed to the transformer now the transformer will use a something called attention mechanism (multi head attention, to be precise) to look at the data and understand the context, the attention mechanism convert the input into 3 variants, that is key, query and value, it then uses a mathematical formula (Q.Kt / dimension(k)) \* V now from here the data will pas through different layer based on whether its encoder part or the decoder part the decoder part has an additional layer called masked multi-head attention other than that, the layers all the same th
• u/scott_codie ▲0Just crunch through the math.

Passkey 피싱으로 Microsoft 365 세션 토큰을 탈취하는 공격 ↗

댓글에서는 인증 자체를 깨는 대신 정상 MFA 뒤에 발급된 세션 토큰을 훔치면 공격자가 동일한 권한과 API 범위로 Microsoft 365 데이터에 접근할 수 있다는 구조가 핵심으로 정리됐습니다. 한 댓글은 민감 필드를 사전에 토큰화하면 세션이 유효해도 이메일·연락처·파일에서 바로 쓸 수 있는 개인정보가 나오지 않게 할 수 있다는 데이터 계층 대응을 들었습니다.

• u/No-Conclusion3720 ▲1RuntimeAI's PII Shield pre-tokenizes sensitive fields before they're queryable, so when the attacker in this campaign used the harvested M365 session token to pull email content, contact records, and files, those reads would have returned structured tokens rather than actual PII — the session was valid, the API calls succeeded, but there was no actionable plaintext to exfiltrate. The breach of the session could still happen. The data theft at that exact read moment couldn't. [https://runtimeai.io](https://runtimeai.io)