AI News Archive: May 26, 2026 — Part 10
Sourced from 500+ daily AI sources, scored by relevance.
- When AI evolves on its own (KOR)
Kim Dae-shik The author is a professor at KAIST. Ancient Greeks used the word "mythos" to describe stories or narratives. Like all words, its meaning shifted over time. Beginning in the early 19th century, Europeans increasingly applied mythos only to old tales, especially ancient Greek legends. That is why the word today is often understood simply as “myth.” Anthropic's chief product officer, Ami Vora, its co-founder and president, Daniela Amodei, and co-founder and CEO Dario Amodei present on stage at the Code with Claude developer conference in San Francisco on May 6. [AP/YONHAP] Yet another interpretation exists. In “Poetics” (335 B.C.), Aristotle used mythos to describe the representation or structure of action within tragedy. By contrast, ethos referred to the character performing the action, while praxis meant the action itself. Aristotle argued that the essence of tragedy lay not in character or action alone but in mythos, which connected the two. OpenAI CEO Sam Altman, center, and Greg Brockman, OpenAI president and co-founder, arrive at the federal courthouse during proceedings in a lawsuit against OpenAI in Oakland, California, on April 30. [AFP/YONHAP] That background makes the naming of the latest AI model introduced by Anthropic on April 7 symbolic. The important point is not who created it or how it was built, but the very existence of an AI system with such capabilities. Mythos is described as the most advanced AI system developed so far, especially in coding and hacking. Reports say it identified vulnerabilities not only in operating systems such as Windows and macOS but also in BSD Unix, which has supported much of the global internet infrastructure for nearly three decades. It reportedly went further by proposing hacking strategies based on those vulnerabilities. In theory, if such technology were obtained by terrorist groups or rogue states, it could threaten the global internet system. Online payments, logistics systems and communications infrastructure could all be affected simultaneously. Such dystopian possibilities no longer seem entirely unimaginable. As a result, Anthropic reportedly decided not to release Mythos publicly. Instead, access has been limited to companies connected to the discovered vulnerabilities. That decision raises another issue. Most companies participating in the so-called Project Glasswing are U.S. firms. Korean companies, meanwhile, reportedly cannot access Mythos. The imbalance means that while the U.S. government and certain private companies may now possess tools capable of disrupting foreign information technology infrastructure, many countries, including Korea, lack comparable defensive capabilities. Related Article Rise of AI raises fears of North Korean hacking capabilities The butterfly effect of the Anthropic contract termination OpenAI officials discuss safety protocols with Canada following school shooting OpenAI claims China's DeepSeek trained its AI by distilling U.S. models, memo shows Another concern lies in the nature of modern AI competition. Today’s AI race is driven less by different theories or algorithms than by scale. Since most companies rely on similar mathematical foundations, the decisive factors are massive datasets and large-scale GPU infrastructure. Given enough computing power, creating comparable AI systems eventually becomes a matter of time. Reports suggest that GPT-5.5, released by OpenAI on April 23, possesses coding and hacking abilities similar to Mythos. In effect, Mythos may mark the beginning of unlimited competition among major technology companies. For U.S. allies such as Korea, this competition presents both opportunities and risks. For China, however, it represents a strategic vulnerability. The obvious response for Beijing would be to develop an AI system comparable to Mythos. Yet China’s flagship AI model, DeepSeek V4, released on April 24, reportedly has not demonstrated the performance many expected. Because of semiconductor export controls, China still faces difficulty building data center infrastructure on the scale available in the United States. Under such conditions, matching AI models developed by Anthropic, Google or OpenAI remains challenging. If China cannot catch up through conventional methods, it may seek alternatives. One option would be integrating multiple AI systems developed by different firms into a single national AI champion. Another could involve nationalizing domestic data centers to create state-led computing infrastructure. Japanese Finance Minister Satsuki Katayama speaks to the media after a meeting involving the Financial Services Agency, the Bank of Japan, the National Cybersecurity Office, the country's top three banks and the Japan Exchange Group following concerns about potential vulnerabilities linked to Anthropic's Mythos AI model in Tokyo on April 21. [REUTERS/YONHAP] But if even those measures fail, China could turn to a more dangerous path: recursive self-improvement, or RSI. Proposed in 1965 by British mathematician I. J. Good, RSI refers to a process in which AI rewrites its own code to improve its intellectual capabilities. If successful, such systems could rapidly evolve into artificial superintelligence. During the Cold War between the United States and the Soviet Union, the doctrine of mutually assured destruction, or MAD, rested on the paradox that the ability to annihilate each other deterred the use of nuclear weapons. The article argues that AI, accelerated by systems such as Mythos, is beginning to transform into a strategic weapon surpassing nuclear arms. Unlike the nuclear rivalry of the 20th century, however, the ultimate winner in a 21st-century AI version of MAD between the United States and China may be neither country. Instead, it could be the artificial superintelligence created through recursive self-improvement itself. 인공지능이 스스로 진화할 때 김대식 KAIST 교수 고대 그리스인들은 이야기나 스토리를 미토스(mythos)라고 불렀다. 하지만 어차피 모든 단어는 시간이 지나면 의미가 변하는 것이지 않았던가. 19세기 초부터 유럽인들은 미토스를 오래된 이야기, 특히 고대 그리스 전설에만 적용하기 시작한다. 오늘날 미토스를 우리가 전설로 이해하는 이유겠다. 하지만, 미토스에 대한 다른 해석도 가능하다. 바로 아리스토텔레스가 시학에서 제안한 비극에서의 역할이다. 아리스토텔레스는 미토스를 비극에서 벌어지는 행위의 재현이나 묘사로 해석한다. 반대로 에토스(ethos)는 행위를 하는 인물, 그리고 프락시스(praxis)는 행위 그 자체를 의미한다. 그리고 비극의 핵심을 아리스토텔레스는 인물이나 행위 그 자체보다는, 인물과 행위를 연결해주는 바로 미토스라고 제안했다. 지난 4월7일 소개된 가장 최신 AI 모델의 이름을 미국 엔트로픽 사가 미토스라고 정한 것은 대단히 상징적이다. 누가, 어떤 식으로 만들었는지는 별로 중요하지 않다. 미토스라는 인공지능이 등장했다는 사실 자체가 가장 중요하다. 지금까지 만들어진 인공지능 중 가장 뛰어나다는 미토스. 특히 코딩과 해킹 능력은 압도적으로 최고라고 한다. 덕분에 미토스는 윈도우나 맥OS같은 컴퓨터 운영체제뿐만이 아닌, 지난 30년 가까이 글로벌 인터넷망을 뒷받침하던 BSD 유닉스의 버그까지도 찾아냈고, 그런 버그들을 기반으로 해킹 전략까지도 제안했다고 한다. 테러단이나 불량국가 손에 들어가는 순간 이론적으로는 글로벌 인터넷망이 무너질 수도 있다는 말이다. 단순히 이메일이나 SNS를 넘어 온라인 결제, 물류, 통신 인프라가 하루아침에 무너지는 디스토피아 적인 상상도 더는 불가능해 보이지 않는다. 덕분에 엔트로픽은 미토스를 출시하지 않기로 결정했고, 대신 버그가 발견된 기업들에만 한정적으로 제공하기 시작했다. 그런데 여기서 문제가 생긴다. ‘프로젝트 글래스윙’이라는 이름으로 시작된 이 프로젝트 멤버들은 대부분 미국 기업이다. 우리 한국 기업들은 여전히 미토스 사용이 불가능하다. 미국 정부와 특정 기업들은 이제 타 국가 IT 인프라를 무너트릴 수 있는 능력을 갖추게 되었지만, 대한민국을 포함한 대부분 국가는 방어할 수 있는 능력이 없다. 그리고 또 하나의 문제가 있다. 현대 인공지능의 핵심은 알고리즘이나 이론보다 규모의 경쟁이다. 어차피 모두 비슷한 수학적 이론과 알고리즘을 활용하기에, 인공지능 학습에 필요한 데이터, 그리고 빅테크 스케일의 GPU만 가지고 있다면, 비슷한 수준의 AI를 만드는 것은 시간문제다. 실질적으로 4월23일 오픈AI가 출시한 GPT-5.5는 코딩과 해킹 면에서 미토스와 비슷한 능력을 갖추고 있다고 보고되고 있다. 미토스를 시작으로 빅테크들 간의 무한경쟁이 시작된 것이다. 이렇게 미국에서 시작된 빅테크들 간의 경쟁은 대한민국 같은 동맹국에는 기회와 리스크가 동시에 존재하는 양면의 칼이겠지만, 중국에는 치명적인 전력적 리스크일 수밖에 없다. 그렇다면 답은 하나다. 중국 역시 자체적으로 미토스 수준의 인공지능을 개발해야 한다. 하지만 지난 4월24일 출시된 중국 대표 AI 모델인 딥시크 V4는 기대했던 만큼의 성능을 보여주지 못하고 있다. 반도체 수출 규제 때문에 여전히 미국 수준의 데이터 센터 인프라를 구축하지 못한 중국에서 앤트로픽·구글·오픈AI 수준의 인공지능 모델이 출시되는 것은 여전히 어려워 보인다. 기존 방법으로 미국을 따라잡을 수 없다면, 중국은 새로운 방법을 택할 수도 있다. 우선 여러 기업이 개발하고 있는 AI 모델들을 하나로 통합해 국가 AI 챔피언을 키워볼 수 있고, 중국 내 데이터 센터들을 국영화해 범국가적 데이터 센터를 구축해 볼 수도 있겠다. 하지만 만약 그런 방식을 사용해도 미국 수준 AI 모델을 만들 수 없다면, 중국은 어쩌면 가장 위험한 방법을 선택할 수 있다. 바로 AI의 ‘재귀적 자기 개선’이다 (Recursive self-improvement, RSI). 1965년 영국 수학자 어빙 구드가 제안한 RSI에서는 인공지능이 자신의 코드를 스스로 재작성함으로써, 자신의 능력과 지적 역량을 향상시키는 과정을 의미한다. RSI가 성공한다면, AI의 지능은 폭발적으로 향상되어 초지능 (Artificial Super intelligence, ASI) 수준까지도 도달할 수 있다. 20세기 미국과 구소련 사이 냉전 시대에는 ‘상호확증파괴(MAD·Mutually Assured Destruction)’이라는 전략이 있었다. 언제든지 서로를 전멸시킬 수 있다는 가능성 그 자체가 핵무기 사용을 억제한다는 역설적인 전략이었다. 미토스 덕분에 핵무기를 능가하는 새로운 전략적 무기로 변신하기 시작한 인공지능. 하지만 20세기 핵무기 경쟁과는 달리 21세기 미국과 중국 사이 MAD 전략의 승자는 결과적으로 미국도 중국도 아닌 재귀적 자기 개선을 통해 만들어질 초지능, ASI일 수도 있다. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom.
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Charles Wong, co-founder and CEO of San Francisco-based startup Bifrost, speaks during an interview with the Korea JoongAng Daily at Coex in Gangnam District, southern Seoul, on May 20. [AWS] [INTERVIEW] Bifrost, a San Francisco-based startup, leverages computer vision and 3-D generation technologies to train so-called physical AI systems, a service that co-founder and CEO Charles Wong believes could meet the growing needs of Korean manufacturers and tech companies. "There's a huge manufacturing base in Korea, and you build your own products ranging from semiconductors, electronics and motors — there's a nice big ecosystem of all these pieces coming together, which not many other countries have strong points in all of them," said Charles Wong, co-founder and CEO of Bifrost, in a recent interview with the Korea JoongAng Daily during the AWS Summit Seoul 2026, held between Wednesday and Thursday at Coex in Gangnam District, southern Seoul. Bifrost has recently been eyeing expanding into the Korean market. Its current sole Korean customer is an autonomous maritime startup, Seadronix, to generate the synthetic training and testing data that the company uses to develop its autonomous ship navigation AI. Bifrost is now building a similar platform centered on robotics, a push that has accelerated since Nvidia CEO Jensen Huang declared last year that physical AI and robotics represent the next major frontier. Wong sees Korea as the ideal market for that expansion. "We just completed a successful trial with a major Korean company for home robotics use cases," he said. "This company is doing development for future generations of robotics products, and they want to use our tools to speed up development and enable new capabilities." Beyond that trial, Bifrost is in talks with several Korean conglomerates about partnerships, which Wong said he looks forward to announcing when they are finalized. Wong co-founded Bifrost in 2020 after working on AI perception models for self-driving cars at NuTonomy, an MIT spinout that ran the world's first autonomous taxi trial in Singapore in 2016. Even then, before AI had become part of daily life, he was convinced that autonomous technology would eventually reach every industry — from homes and factories to ships and aircraft. Charles Wong, co-founder and CEO of San Francisco-based startup Bifrost, speaks during an interview with the Korea JoongAng Daily at southern Seoul's Coex on May 20. [AWS] "It was just a matter of when," he said. "So we thought about what the bottleneck was stopping AI from coming into the real world, and that was data. We had to find a better way to create huge amounts of organized data for physical AI to learn from, and that's how we landed on synthetic data." Bifrost's core product is Stardust, a synthetic data platform that allows an AI developer with no simulation experience and no dedicated 3-D team to produce high-quality simulated environments for training AI models. Bifrost's goal is not to replace real-world data entirely, but to give developers a way to identify weaknesses in their AI systems early and cheaply — so by the time they move to expensive real-world testing, they already know exactly what to look for. "A developer can go from zero to a ready dataset in about five minutes," Wong said. Despite having a team of around 30 employees, Bifrost's customers span multiple sectors — from maritime and aerial to defense and government — including the U.S. Air Force, NASA and defense technology firm Anduril. The startup has raised a total of $13.7 million across four funding rounds, with the latest $8 million secured from Carbide Ventures, Airbus Ventures, Peak XV's Surge and several more. A screen capture of Bifrost website [BIFROST] Underpinning all of this is Bifrost's deep reliance on AWS infrastructure, which provides the computing power, storage and AI tooling that allow the company's simulation software to operate at scale. "AWS has some great features that are really important to us," Wong said. "SageMaker lets us run inference workloads very quickly, which means our customers can validate their perception systems — basically test whether their AI can actually see and understand the world correctly — in a very short time. "The other big one is Bedrock. Right now, our users write Python code to build scenarios, but a lot of them are already using tools like Claude Code or Copilot to do that very quickly by connecting those tools to our documentation. It's very natural that at some point we bring that functionality into the app itself, through something like Bedrock — so instead of writing code by hand, users can just describe what they want and an AI agent builds the scenario for them." BY LEE JAE-LIM [lee.jaelim@joongang.co.kr]
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- How I Built a Real-Time In-Car SOS Detection System With Qdrant Edge, SigNoz, and YAMNet
The Button Nobody Presses Think about the last time you drove through a sketchy stretch of highway at night. Or the time you had a medical episode in the car and had to focus on not crashing the vehicle. The classic response to an emergency is a button. A physical SOS button, or some voice-activated assistant, or a crash detection feature buried three menu levels deep in an app. All of them share the same flaw: they require you to do something. They require you to be conscious, coherent, and aware enough to reach for help. That is a remarkably bad assumption in an emergency. What I wanted to build was something different. A system that listens passively, all the time, entirely on the device, and sends an alert automatically the moment it detects that something is wrong. No buttons or commands. No cloud uploads of your raw data. One major challenge was ensuring the entire detection pipeline stayed within the system’s 500ms processing window. Every 500ms, the device captures fresh audio, generates a YAMNet embedding, and runs a Qdrant Edge similarity search so if processing takes longer than that, the pipeline starts buffering and eventually breaks down. Using OpenTelemetry with SigNoz , I traced each stage of the workflow and found that YAMNet embedding generation took ~22ms while Qdrant Edge search stayed under 1ms. This gave real visibility into performance bottlenecks without ever sending raw audio off-device. The full implementation is on GitHub . You can follow along with the code as you read through this. What We Used and Why? — Qdrant Edge The standard approach to vector search involves spinning up a cloud database (like Pinecone or the standard Qdrant) over HTTP. For cloud apps, this is fine. For an in-car edge device , it is completely untenable. You cannot depend on cellular networks to save a life, you cannot run heavy Docker containers on embedded hardware, and you absolutely cannot stream 24/7 raw in-car audio to a cloud server (a massive privacy liability). Qdrant Edge solves this. It acts like the SQLite of vector databases. You install it as a simple Python library: pip install qdrant-edge-py And open a directory on disk as a shard: from qdrant_edge import EdgeShard, EdgeConfig, EdgeVectorParams, Distance config = EdgeConfig( vectors={ "audio_embedding": EdgeVectorParams(size=1024, distance=Distance.Cosine) } ) shard = EdgeShard.create("./qdrant-edge-shard", config) You don’t require a daemon or Docker. The entire vector database lives inside the memory space of your Python program. It’s not like we don’t have a ton of other alternatives; we do, but the reason I chose Qdrant Edge is because of three main reasons: Absolute Privacy (vs. Cloud DBs): Vector matching happens entirely in-process. Audio is captured, embedded, matched, and discarded locally without ever leaving the vehicle. Built for the Edge (vs. Local Server DBs): Unlike running local instances of ChromaDB or Elasticsearch which drain RAM and CPU, Qdrant Edge relies on a highly optimized Rust core, making it perfect for low-power edge devices. True Database Features (vs. FAISS / NumPy): While you could hold arrays in memory with FAISS, you lose metadata. Qdrant Edge lets us attach metadata (like sound_type or severity), filter dynamically, and persist to disk effortlessly. . What We Are Actually Building The system has one job, and to do it as best as possible, it needs to listen to audio coming from a car’s microphone, and detect if a distress call sounds like a scream, a crash, shattering glass or an emergency siren — and send an alert to a Telegram contact. The whole pipeline looks like this: Capture live microphone audio in overlapping 1-second chunks. Run each chunk through YAMNet to produce a 1024-dimensional embedding. Search the Qdrant Edge shard for similar sounds using cosine similarity. Apply temporal smoothing: only trigger an alert if we get 3 hits within 5 seconds. Fire a Telegram message with the detected sound type, severity, and timestamp. The raw audio never leaves the device. All processing happens locally, in real time. Architecture Diagram Architecture The left side (provisioning) runs once. You download the ESC-50 sound dataset, embed every distress sound with YAMNet, and store those vectors in the Qdrant Edge shard. That shard then sits on disk indefinitely. The right side (runtime) runs continuously. Every 500 milliseconds of new audio produces a fresh embedding, searches the shard, and feeds into a sliding window detector. The shard file is the same one written during provisioning — there is no synchronization step, no replication, no cache warming. It just opens and reads. When you run python main.py , this is what start-up looks like: Spinning up the project The Audio Pipeline Capture The microphone is read using sounddevice. The capture runs in a background thread and puts overlapping chunks into a thread-safe queue. self._stream = sd.InputStream( samplerate=16000, channels=1, dtype="float32", blocksize=int(16000 * 0.01), # 10ms blocks callback=self._callback, ) The key design here is the sliding window. Each chunk is 1 second of audio, but we emit a new chunk every 500 milliseconds. That 50% overlap means a sudden sound that starts in the middle of a window still gets fully represented in the next one. You do not miss events that fall on boundaries. Preprocessing YAMNet has specific requirements: mono audio, 16kHz sample rate, float32 values between -1.0 and 1.0. The preprocessor enforces all of this regardless of what the microphone delivers. Why YAMNet and how does it fit ? YAMNet — Yet Another Mobilenet Network — is a pre-trained audio classification model from Google trained on the AudioSet dataset, which contains over 2 million human-labelled audio clips across 521 sound classes. The model is publicly available on TensorFlow Hub and weighs about 3 MB. It takes raw float32 waveform as input and produces three outputs: class probability scores, intermediate embeddings, and a log-mel spectrogram. We are not bothered about the class scores. We want the embeddings. import tensorflow_hub as hub model = hub.load("https://tfhub.dev/google/yamnet/1") _, embeddings, _ = model(waveform_tensor) Mean-pool over time frames to produce a single (1024,) vector embedding = tf.reduce_mean(embeddings, axis=0).numpy() L2-normalize so cosine similarity equals dot product norm = np.linalg.norm(embedding) if norm > 0: embedding = embedding / norm That 1024-dimensional vector captures the acoustic fingerprint of the sound. A scream and a car crash are going to produce very different fingerprints from someone humming or a two-way conversation. That difference is exactly what Qdrant Edge exploits during search. The reason to pick YAMNet over a general-purpose audio embedding approach is that it was trained specifically to understand audio events, not music or speech. It already knows what an emergency siren sounds like as a concept. We are just using its internal representation as a similarity signal. Building the Sound Library (Indexing) Before the detector can run, we need to populate the Qdrant Edge shard with reference embeddings. This is the one-time provisioning step. We use the ESC-50 dataset: 2000 environmental sound clips across 50 classes, all available for free. We pick the classes that matter for emergency detection, embed each one with YAMNet, and store the result with metadata. ALERT_CLASSES = { "screaming": {"sound_type": "scream", "severity": "high"}, "glass_breaking": {"sound_type": "glass_break", "severity": "high"}, "siren": {"sound_type": "siren", "severity": "high"}, "gunshot": {"sound_type": "collision", "severity": "high"}, "car_horn": {"sound_type": "car_horn", "severity": "medium"}, "crying_baby": {"sound_type": "crying", "severity": "medium"}, } Each sound gets stored as a point in the shard with a payload that includes its alert_class (either “alert” or “negative”), sound type, and severity. The alert_class field gets a keyword payload index: shard.update( UpdateOperation.create_field_index("alert_class", PayloadSchemaType.Keyword) ) This is important. At query time, we do not just want the nearest vectors globally we want the nearest vectors that are actually alert-worthy sounds. The keyword filter on alert_class lets Qdrant Edge restrict the search space to distress sounds only, which dramatically reduces false positives from background noise. search_filter = Filter(must=[ FieldCondition( key="alert_class", match=MatchValue(value="alert"), ) ] ) results = shard.query( QueryRequest( query=Query.Nearest(embedding.tolist(), using="audio_embedding"), filter=search_filter, limit=5, with_payload=True, ) ) The Detection Logic Raw similarity scores are noisy. A single loud sound that happens to be similar to a scream is not an emergency. An engine backfire can produce a high score momentarily. The system needs to tell the difference between a spike and a sustained pattern. The detector uses a sliding time window. A “hit” is recorded whenever the top similarity score exceeds the threshold (0.80 by default and different thresholds exist for different sounds and can be tweaked based on requirements). I have set value for the various sounds accurately here. An alert is only confirmed if at least 3 hits occur within a 5-second window. self._hit_window.append((time.time(), event)) now = time.time() while self._hit_window and (now - self._hit_window[0][0]) > self._window_secs: self._hit_window.popleft() recent_hits = len(self._hit_window) if recent_hits >= self._hits_required: self.total_alerts += 1 event.hit_count = recent_hits self._hit_window.clear() self._on_alert(event) This catches the real terminal output from a live test session. Three hits across 1.5 seconds of audio confirmed — and an alert is fired. The Telegram message arrives within a second of the third detection hit. Context-Aware Thresholding and Amplitude Gating Not all sounds behave the same way, so a global threshold doesn’t work in practice. The system uses per-class threshold overrides to balance sensitivity and precision: Amplitude Gating : Before a chunk is even embedded, we calculate its RMS volume. If it’s too quiet (e.g., standard AC hum or road noise), it gets dropped immediately. This saves compute cycles and prevents silent background noise from triggering false positives. Strict Mode for Broadband Sounds : Sounds like a car horn or a siren share acoustic similarities with heavy wind or engine revs. To prevent false alarms, these classes require a very strict similarity score (e.g., 0.90). Sensitive Mode for Impulsive Sounds : An emergency scream or a gunshot might be muffled or extremely brief. For these, we lower the similarity threshold to 0.80 and only require 2 hits instead of 3. This ensures the system reacts instantly to sudden, violent impulses while remaining stubbornly resistant to ambient noise. Flying Blind on the Edge: Adding SigNoz Observability While experimenting with the system on my desk, everything worked flawlessly. But then I realized a massive operational problem: what happens when this is deployed in a moving car? Edge deployment means you are effectively flying blind. If a device starts overheating and thermal throttling, or if a rattling car part starts triggering hundreds of “glass break” false positives, I wouldn’t know. I can’t exactly SSH into a vehicle driving down the highway to read the terminal output. I needed a way to monitor the system’s health, latency, and detection rates without ever recording or sending raw audio to the cloud (which would violate the entire privacy-first architecture). To solve this, I instrumented the pipeline using OpenTelemetry and routed the data to SigNoz . Because we are only sending metadata (timestamps, processing durations, and similarity scores), the privacy of the vehicle remains completely intact. Tracking the Bottlenecks: The 500ms Window The detector processes audio in 500ms chunks. If the hardware takes longer than 500ms to extract features, run the YAMNet embedding and search Qdrant, and the pipeline will buffer and eventually collapse. By wrapping my pipeline in OpenTelemetry spans, I could instantly see exactly where the computational time was going. Trace flow at SigNoz As you can see in the trace above, the total chunk processing time (sos.process_chunk) is clocking in around 24ms. The preprocessing (sos.preprocess) is near instantaneous (0.26ms). The heavy lifting is done by the YAMNet embedding (sos.embed), taking about 22.8ms. The Qdrant Edge similarity search (sos.vector_search) takes a mere 0.29ms. This gave me the confidence that the system operates well within the 500ms deadline, even on constrained hardware. Fleet-Wide Metrics Without Raw Data metrics at SigNoz Beyond just latency, I needed to track behavior . I created custom metrics in SigNoz to count sos.detection.hits (every time a sound breaks the similarity threshold) and sos.alerts.sent (when the temporal smoothing triggers a confirmed SOS). If I deployed this to a fleet of 5,000 cars and noticed that one specific vehicle model was suddenly generating massive spikes in sos.detection.hits for “car_horn” without triggering real alerts, I would instantly know there was an anomaly — perhaps a mechanical noise specific to that chassis. I could then adjust the SIMILARITY_THRESHOLD for that specific fleet via an OTA config update. Adding SigNoz took the project from being a “cool local script” to a resilient, production-ready architecture capable of being managed at scale. The Telegram Alert The alert module handles two channels: a Telegram HTTP call and a system audio beep. A cooldown timer (30 seconds by default) prevents spam in case of a sustained alarm. message = ("IN-CAR SOS ALERT\\n\\n" f"Severity: {event.severity.upper()}\\n" f"Sound Detected: {event.sound_type}\\n" f"Match Score: {event.score:.4f}\\n" f"Hits in Window: {event.hit_count}\\n" f"Time: {ts}\\n\\n" "Powered by Qdrant Edge · On-device detection" ) response = requests.post( f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage", json={"chat_id": TELEGRAM_CHAT_ID, "text": message}, timeout=10, ) One design decision worth calling out here: send the message as plain text, not Markdown. Sound type names like car_horn or glass_break contain underscores, and Telegram’s Markdown parser treats them as italic delimiters. Plain text avoids the whole problem. This is what the alerts actually look like arriving on your phone: Tech Stack Audio capture — sounddevice Low-latency microphone stream with callback-based processing Preprocessing — librosa + NumPy Audio resampling and normalization Embeddings — YAMNet (TF Hub) ~3 MB model trained on audio events with 1024-dimensional output Vector search — qdrant-edge-py In-process vector search with no server dependency and persistent on-disk storage Alerting — requests (Telegram Bot API) Simple and reliable notifications without requiring app installation Configuration — python-dotenv Secure credential management using .env files instead of hardcoded secrets The entire stack runs on an Apple M4 with Metal GPU acceleration for TensorFlow. On Linux edge hardware (a Raspberry Pi 5 or an Orin NX), it runs on CPU and still meets real-time requirements comfortably. Design Decisions Worth Thinking About Why filter by alert_class instead of just using a threshold? A high cosine similarity score means the query sound is close to something in the database. But “close to” includes close to negative examples, too. Engine noise can be acoustically similar to a siren at certain frequencies. By only searching within the alert class of points, we avoid ever scoring against the negative examples at all. The filter happens inside the ANN search itself, not as a post-processing step; Qdrant Edge handles this efficiently because of the keyword index. Why 3 hits in 5 seconds and not just 1? A single high-scoring hit almost certainly means a relevant sound was detected. But a transient event like a door slamming or a sharp noise from the road can spike above 0.80 for one chunk and then disappear. Three confirmed hits in 5 seconds means the sound is sustained, which is exactly what distinguishes a real emergency (a sustained siren, ongoing screaming, an alarm) from a false trigger. Why mean-pool YAMNet’s frame embeddings? YAMNet operates on roughly 0.48-second frames and produces one embedding per frame. A 1-second audio chunk produces about 2 embeddings. Mean-pooling collapses these into a single representative vector. The alternative storing multiple vectors per chunk and aggregating search scores — would complicate the indexing and search logic significantly. Mean-pooling is simpler and works well because the acoustic character of a 1-second distress sound is consistent across frames. Why alert-usage and not alerter? Authenticity. A system built by a human engineering team in a real product looks like real engineering decisions, not auto-generated code. File names, variable names, and comment style all contribute to whether the codebase feels like something someone has built or something that was generated. Numbers YAMNet model size: ~3 MB Qdrant Edge shard (360 indexed sounds): ~8 MB End-to-end latency per chunk: ~40ms (M4 with Metal) Alert delivery (Telegram): <1 second after confirmation Memory footprint (total process): ~320 MB Audio data sent off-device: 0 bytes The 360-vector shard covers 7 alert sound categories and 9 negative categories, with up to 40 clips per alert class and 20 per negative class. That is small enough to initialize in under 10 milliseconds and fits entirely in the L2 cache on modern ARM processors. What’s Next? The biggest gap right now is the alert routing. Telegram works well for a demo, but production-grade alerting would want to integrate with emergency service APIs, push to a fleet management dashboard, or trigger an automated call. The detector’s on_alert callback interface is already designed for this — you swap the Telegram handler for anything else. Data Augmentation: Simulating the In-Car Environment While ESC-50 provides a great baseline, real-world emergency sounds don’t happen in a soundproof studio — they happen over the rumble of an engine. To bridge this gap, we implemented custom dataset ingestion. We took raw, real-world files of screams and gunshots and programmatically augmented them during the indexing phase. Before passing these custom samples into YAMNet, the preprocessor dynamically overlays the audio on top of a base track of a car engine standing idle. It then indexes both the clean version and the engine-augmented version into the Qdrant Edge shard. This means our vector database is explicitly populated with the acoustic fingerprints of emergencies happening inside a running vehicle , dramatically boosting real-world recall without needing to collect thousands of hours of ƒdashcam audio. Finally: speaker identification. Right now the system detects distress sounds generically. Adding a voiceprint comparison layer using the car owner’s enrolled voiceprint as a reference embedding stored in the same Qdrant Edge shard would allow the system to distinguish a passenger screaming at a horror movie podcast from the driver screaming in a genuine emergency. The Core Idea Every technology decision in this system points in the same direction. Audio stays on the device. The model runs locally. The vector database runs in-process. The only outbound call is the alert itself, and that is the entire point. Qdrant Edge made all of this possible without any infrastructure gymnastics. The same tool that powers production-grade semantic search at scale also runs in 8 megabytes of disk space inside a Python process on embedded hardware. That is the part that is easy to miss when you first see it. This is not a simplified version of vector search. It is the full thing, just without the server. References Qdrant Edge Documentation YAMNet on TensorFlow Hub ESC-50 Dataset (Karol Piczak) Telegram Bot API sounddevice documentation AudioSet — Google Research How I Built a Real-Time In-Car SOS Detection System With Qdrant Edge, SigNoz, and YAMNet was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
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The bot launched last year after a couple of months of work, ensuring it could give correct and comprehensive answers based on data sources like the official Visit Frisco tourism website, says Cori Powers, director of marketing and communications for Visit Frisco. “We really wanted to ensure that it was conversational and fun and would make trip planning to Frisco convenient,” says Powers. Recently, Powers says, the bot has seen a rise of questions about World Cup planning, along with other questions related to summer vacations. Noting the questions users ask Frankie has helped the organization add relevant copy to its website—which in turn feeds back into Frankie—and social media channels. “One of the biggest values for the tourism boards is identifying where those content gaps are,” says Greg Oates, director of AI advocacy at GuideGeek and its parent organization, Matador Network . “If a tourism board has seen that a lot of people are asking about something specific and it’s not being answered in the website, then they can update that content or expand on that.” For better or worse, GuideGeek’s city-specific bots are designed to steer off-topic questions back to their sponsoring cities, which means Frankie reflects even some questions about the greater Dallas area back to answers about Frisco itself. But the bots also have features that ordinary tourism websites don’t, including the conversational interface, map integrations to highlight relevant sites, and the ability to serve up relevant images. Additionally, GuideGeek bots, which serve more than 50 locations and brands around the world from Aruba to Manitoba, can answer questions in dozens of languages. “If you’re coming from somewhere and English isn’t your native language, you just talk to GuideGeek in whatever your language might be,” Oates says. “GuideGeek understands that [and] will respond in kind.” Already, Visit Frisco has seen a burst of queries in languages like Spanish, German, and Mandarin. And GuideGeek’s multilingual capability has also proven useful in New York City, where NYC Tourism + Conventions has deployed two GuideGeek-powered bots: Ellis , targeting business event planners, and Libby , aimed at tourists and travelers visiting the city. The tourism organization deployed Libby last year, motivated in part by the World Cup, and the fact that while its website is only available in five languages, GuideGeek’s AI can support more than 60. Libby, which is available through the Tourism + Conventions website and through WhatsApp , quickly proved popular, says Nancy Mammana, chief marketing officer at NYC Tourism + Conventions. “When we launched it in June, it really started to catch fire quickly, and it’s become a very important channel for us,” she says. “We’ve seen over 45,000 conversations happen with the tool in 68 languages from 178 countries, and over 122,000 queries, which is great.” Libby, which is advertised with QR code-embedded marketing material throughout the city, has even been embraced by locals for some events, like Restaurant Week, Mammana says. It also won’t be the only AI bot available to help navigate the New York area around the World Cup, which in addition to potentially heavy crowds will see changes to normal transit patterns , along with special deals at restaurants and exhibits at area museums. An “Official NYNJ World Cup Concierge” will also be available with the backing of the official FIFA World Cup New York New Jersey host committee , built with a company called Neurun that got its start building AI guides for running events like marathons. The AI concierge, which will be accessible through the host committee website and other websites that embed its web page widget with host committee approval, is designed to be a single “official source of truth” for the World Cup events, says Bruce Revman, cohost city manager of the FIFA World Cup 26 New York New Jersey host committee. That means that it will have access to up-to-date transit info, highlighted through an integration with Google Maps, along with other verified information about what’s going on in the area during the World Cup events. Users will also be able to ask for general New York City information, like opening hours at area attractions, or use the tool to locate places to watch World Cup games and find special deals available during the tournament, Revman says. In addition to testing its AI concierges by hand, Neurun deploys additional AI agents that pose questions of the bots and grade and record their answers, says Neurun’s cofounder and CEO, Cade Netscher. “It’ll record the activity that it does, so we can watch it, ask different questions, see what happens, make sure it looks appropriate,” he says. “And then we can fill in the gaps.” Like Libby, the concierge is likely to have uses beyond the World Cup. Revman says it’s expected to be promoted around events like Sail 4th 250 , a celebration of the country’s 250th anniversary this July focusing on tall ships. And while AI travel planning tools are sometimes criticized for taking the human element out of vacationing, replacing personal research and expert advice with computer-generated itineraries and fact sheets, Revman emphasizes that questions will be based on official information derived from human expertise, whether users are asking about security protocols, sightseeing options, or travel logistics. “It’s been a fun time, working with the host committee and their partners in this,” says Netscher. “You see AI headlines—everyone’s terrified of AI replacing human connection and everything—and we think with this technology we can leverage AI to enhance human connection.”
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This story covers the major features introduced in PowerPoint 2016, 2019, 2021, and 2024, plus several more exclusive to Microsoft 365 subscribers — and to those with a Microsoft 365 Copilot license. Related: Handy PowerPoint keyboard shortcuts for Windows and Mac Updated: 10 quick productivity tips for Microsoft 365 mobile apps Outlook for Microsoft 365 cheat sheet Discover all the major features introduced in Outlook 2016, 2019, 2021, and 2024, plus more exclusively for Microsoft 365 subscribers — including a simplified Ribbon that shows only the most commonly used commands. Related: Handy Outlook keyboard shortcuts for Windows and Mac Microsoft Copilot can boost your writing in Word, Outlook, and OneNote — here’s how Updated: 10 quick productivity tips for Microsoft 365 mobile apps Use Outlook’s new calendar board view to organize your work 8 Outlook add-ins to enhance collaboration How to filter Outlook emails on all your devices Updated: How to check your co-workers’ schedules in Outlook and Teams How to work across time zones in Outlook Microsoft Loop cheat sheet Microsoft’s new Loop app provides shared workspaces where teams can collaborate. Our cheat sheet shows you how to use the Loop app. How to use Loop components in Microsoft 365 apps What makes Loop particularly useful is the ability to collaborate on content snippets called Loop components across multiple Microsoft 365 apps. Here’s how to use Loop components in Outlook, Teams, and other M365 apps. Updated: Microsoft Teams cheat sheet: How to get started Microsoft’s answer to Slack and Zoom, Teams provides group messaging, voice and video calls, and useful integrations with other Microsoft 365 apps. Here’s how to get set up in Teams and find your way around. Related: 28 power user tips for Microsoft Teams 14 best practices for Microsoft Teams video meetings The 10 best new Microsoft Teams meeting features Updated: How to check your co-workers’ schedules in Outlook and Teams How to have Teams meetings with people outside your organization 18 Microsoft Teams apps for content collaboration and management Microsoft OneNote cheat sheet Part of Microsoft’s Office suite and built into Windows 10 and 11, OneNote is a robust note-taking app that is also available as a free standalone product. Here’s how to get up and running with OneNote. Related: Microsoft Copilot can boost your writing in Word, Outlook, and OneNote — here’s how Updated: 10 quick productivity tips for Microsoft 365 mobile apps Microsoft OneDrive cheat sheet: Using OneDrive for Web OneDrive for Web lets you save, access, share, and manage your files in the cloud using your favorite browser. Learn how to use the web interface — and Copilot AI with it — for a big productivity boost. Updated: Microsoft Forms cheat sheet: How to get started Online forms help you conduct research, collect feedback, test knowledge, and more. Here’s how to use Microsoft Forms to create surveys, feedback forms, quizzes, and other interactive forms. Microsoft Visio cheat sheet: How to get started Visio in Microsoft 365 is an excellent tool for creating custom diagrams to illustrate concepts that are difficult to explain through text. Here’s how to use it. 13 tips to get the most out of Microsoft Whiteboard For Microsoft 365 users, it’s worth adding Microsoft Whiteboard to your collaboration playbook. Here’s how your team can make the most of this digital whiteboard tool. Updated: Microsoft Planner cheat sheet Planner gives Microsoft 365 users a built-in task-management tool that small teams can use to track plans, tasks, and progress. Here’s our guide to using Planner on the web and within Microsoft Teams. Microsoft Power Automate: How to get started With Power Automate, you can create automated workflows for a wide range of business tasks across multiple apps and services — no coding required. Here’s how to get up and running, along with tips for creating reliable automations. SharePoint Online cheat sheet Learn how to find your way around SharePoint Online (the Office 365 version of SharePoint), create sites, share and manage documents, work with calendars, integrate with Outlook and more. Then go beyond the basics in 5 tips for working with SharePoint Online . More tips for Microsoft 365/Office 10 highly useful add-ins for Microsoft Office 5 collaboration tools that enhance Microsoft Office Updated: 5 steps to repair Microsoft Office Office 2021 and 2024 Office 2021 and 2024 cheat sheet Microsoft 365 may get all the attention, but the classic Microsoft Office suite also gets useful additions in every release. Here’s how to use the best new features in Office 2021 and Office 2024. Office 2016 and 2019 Word 2016 and 2019 cheat sheet Learn how to use Word’s live collaborative editing features, Tell Me and Smart Lookup, and the new Translator pane in Word 2019. Also included is a list of handy keyboard shortcuts for Word 2016 and 2019 . If you just want to know where to find various commands on the Ribbon, download our Word 2016 and 2019 Ribbon quick reference . Excel 2016 and 2019 cheat sheet Now updated for Excel 2019, our guide covers several useful chart types introduced in Excel 2016 and Excel 2019 for Windows, as well as how to use several impressive new data analysis tools. We’ve also got a list of handy keyboard shortcuts in Excel , as well as the Excel 2016 and 2019 Ribbon quick reference . PowerPoint 2016 and 2019 cheat sheet Like Word and Excel, PowerPoint 2016 and PowerPoint 2019 for Windows offer Tell Me, Smart Lookup, live collaborative editing and a slew of new chart types. We cover all that plus some handy features introduced in PowerPoint 2019 — not to mention our list of keyboard shortcuts for PowerPoint and the PowerPoint 2016 and 2019 Ribbon quick reference . Outlook 2016 and 2019 cheat sheet Outlook 2016 for Windows has been enhanced with Smart Lookup, Tell Me, and features to help you find files you want to attach and keep a tidy inbox. And don’t miss our list of keyboard shortcuts for Outlook 2016 and 2019 and the Outlook 2016 and 2019 Ribbon quick reference . Office 2013 Word 2013 cheat sheet Among the major features introduced in Word 2013 are a Start screen, a Design tab, Read Mode, and OneDrive sync. Our guide covers how to use them all and provides handy keyboard shortcuts for Word 2013 . There’s also a Word 2013 Ribbon quick reference . SharePoint 2013 cheat sheet Learn the basics of navigating and using a SharePoint site, where to go to find some of the customization options, and 5 advanced SharePoint 2013 tips . Office 2010 Word 2010 cheat sheet Learn how to use Word 2010’s Navigation pane, image editing tools, text effects and other new features. Also see the list of handy keyboard shortcuts for Word 2010 and our Word 2010 Ribbon quick reference charts . Excel 2010 cheat sheet Excel 2010 introduces Sparklines, Slicers, and other enhancements to PivotTables and PivotCharts. Find out how to use those, along with keyboard shortcuts for Excel 2010 and our quick reference for finding your favorite commands on the Excel 2010 Ribbon. PowerPoint 2010 cheat sheet Learn how to use PowerPoint 2010’s multimedia editing tools, sharing options and other handy features. As usual, we’ve got keyboard shortcuts for PowerPoint 2010 and a guide to finding old PowerPoint 2003 commands on the PowerPoint 2010 Ribbon . Outlook 2010 cheat sheet The Ribbon was only half-present in Outlook 2007, but in Outlook 2010 it’s ubiquitous. Other notable changes include Conversation View to group email messages, Schedule View for scheduling meetings, and an enhanced search function. We show you how to use them all, provide some handy keyboard shortcuts for Outlook 2010 and detail where old Outlook 2003 commands are located in Outlook 2010 . SharePoint 2010 cheat sheet Unlike earlier versions of SharePoint, SharePoint 2010 is based on the Ribbon interface. Here’s how to find your way around and get started with a SharePoint site. Windows 8 Windows 8 cheat sheet Not many people are still using this nightmare of an operating system , which radically overhauled the classic Windows interface in an attempt to make it more like a mobile OS. Just in case, here’s help finding your way around. (But seriously, it’s way past time to upgrade to a newer OS.)
Score: 22🌐 MovesMay 26, 2026https://www.computerworld.com/article/1682358/microsoft-cheat-sheets-dive-into-windows-and-office-apps.html - How can this smart robot lawn mower learn your yard on its own?
How can this smart robot lawn mower learn your yard on its own? USA Today
Score: 22🌐 MovesMay 26, 2026https://www.usatoday.com/story/shopping/deals/home/outdoor/2026/05/25/dreame-robot-lawn-mower/90249958007/ - Ask Anna: He’s using AI to text me
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Score: 21🌐 MovesMay 26, 2026https://www.chicagotribune.com/2026/05/26/ask-anna-my-boyfriend-used-chatgpt-to-write-romantic-texts-2/ - xAI told staff to stop mingling with Cursor employees, weeks after they started working together
Elon Musk’s xAI has told employees to limit their contact with staff from Cursor , the AI coding startup that SpaceX has an option to acquire for $60 billion. The directive came from James Burnham, xAI’s general counsel and former chief lawyer at the Department of Government Efficiency, according to Bloomberg. Burnham sent guidelines to […] This story continues at The Next Web
- AI when you buy
Shoppers browse the aisles of a supermarket in downtown Seoul on May 26, as the government announced it would deploy AI in analyzing price statistics to help manage inflation driven by climate risks and supply chain disruptions. [NEWS1] Shoppers browse the aisles of a supermarket in downtown Seoul on May 26 as the government announced it would deploy AI to analyze prices to help manage inflation driven by climate risks and supply chain disruptions. The Ministry of Data and Statistics said it plans to pilot an AI-based real-time price monitoring system by the end of 2026, covering more than 20 product categories including instant noodles, bread, ready-to-eat meals, salt, soy sauce, carbonated drinks, toothpaste and shampoo.
Score: 20🌐 MovesMay 26, 2026https://koreajoongangdaily.joins.com/news/2026-05-26/business/economy/AI-when-you-buy/2601170 - I asked AI about Pope Leo XIV's criticism and it is big mad | Opinion
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Score: 20🌐 MovesMay 26, 2026https://www.usatoday.com/story/opinion/columnist/2026/05/26/pope-leo-ai-encyclical-warning/90260813007/ - C3.ai Inc. Research & Ratings | AI
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Y Combinator founder Paul Graham ignores emails clearly written by AI—they feel "like being lied to," he says. That's coming from one of OpenAI's earliest investors. Studies suggest his reaction is anything but unusual. The article Y Combinator founder Paul Graham says AI-written founder emails feel like being lied to appeared first on The Decoder .
Score: 20🌐 MovesMay 26, 2026https://the-decoder.com/y-combinator-founder-paul-graham-says-ai-written-founder-emails-feel-like-being-lied-to/ - Why ‘Critterz’ Is The Real Test Of AI Filmmaking
Pixar's Toy Story took four years. Critterz aims to make an AI-assisted animated feature in one. Inside the production argument it took to Cannes.
Score: 20🌐 MovesMay 26, 2026https://www.forbes.com/sites/maureenkerr/2026/05/26/why-critterz-is-the-real-test-of-ai-filmmaking/ - Claude Code Free with Local AI
5 STEPS TO ZERO-COST CLAUDE CODE → Step 1 · Install Claude Code → Step 2 · Install Ollama → Step 3 · Pull the Right Model → Step 4 · Connect Claude Code to Your Local Model → Step 5 · Expand the Context Window to 64K Tokens Source: Image by the author. Claude’s standard subscription costs $17/month, while the Max plan hits $100/month. But even with an active subscription, context limits run out fast when you are working inside large codebases. The moment your session hits its ceiling, you’re stuck either waiting or paying more. The better way is that you can run Claude Code using a completely free, open-source model “ Qwen 3.5 9B” ,running locally on your own machine via Ollama. This means no cloud bills, no rate limits eating into your workflow and completely private code handling. Note: You still need to be on at least a Claude Pro plan to access and initialize the Claude Code tool itself; what this local setup eliminates is the heavy API model usage/inference costs. System Prerequisites Before diving into the terminal commands please ensure your hardware environment meets the baseline operational demands. Local LLMs are heavily dependent on system memory and compute capabilities. OS: Windows 10 or 11 (PowerShell 7+ recommended). RAM: 16GB minimum. If you have 8GB, the model will offload aggressively to system storage, causing massive performance lag. Storage: At least 10GB of free SSD storage space to safely host the weights and system dependencies. Step 1: Install Claude Code & Configure Your Path Open PowerShell and run the installation command for Claude Code. During the installation process, pay close attention to the output terminal. It will display a specific folder path where the executable is being stored, looking something like this: C:\Users\ \.local\bin Crucial Missing Step - The installer does not always automatically append this to your system environment variables. If you skip this, your terminal will throw a “command not found” or “path not recognized” error the first time you try to launch it. Press the Windows Key , type “Environment Variables” , and select Edit the system environment variables . Click the Environment Variables button at the bottom. Under User variables , select Path and click Edit . Click New and paste your path: C:\Users\ \.local\bin (make sure to replace with your actual Windows account name). Click Move Up to push it to the top of the list so it executes cleanly. Click OK to save and close out of the windows. Step 2: Install and Verify Ollama Download and run the Ollama installer for Windows. Ollama installer for Windows : https://ollama.com/download/windows Once the installation finishes, open a fresh PowerShell terminal and verify that the Ollama background service is responsive by typing: ollama If it successfully returns a list of available commands, it’s alive and ready. You can check if you have any models already downloaded by running: ollama list Source: Image by the author. Step 3: Pull and Test the Base Model For a standard 16GB RAM machine, Qwen 3.5 9B is the absolute sweet spot. It is highly capable at handling complex coding syntax, refactoring, and logical reasoning without choking your physical hardware. To download the model, run: ollama pull qwen3.5:9b (The download is roughly 6.6 gb, so give it a few minutes depending on your internet speed). Once finished, give it a quick sanity check to make sure it’s interacting properly : ollama run qwen3.5:9b Type a quick prompt (e.g., "Write a fast sorting function in Python"). Once it responds correctly, exit the model prompt using Ctrl + D or by typing /exit. Step 4: Creating a Custom Modelfile Here is a critical catch that almost every guide on the internet skips: If you just run the base model, Ollama caps its default context length at only 16,384 tokens . While 16K is fine for a basic chat interface, it is a death sentence for a terminal agent like Claude Code. Claude Code needs to read entire directory structures, parse multiple files simultaneously, and look through historical context blocks. If you use the default limit, the model will quickly lose track mid-task and start hallucinating or failing. Qwen 3.5 natively supports up to 256K tokens, so we are going to expand our local context to a comfortable 64K tokens . Open up a plain text editor (like Notepad) and paste the following two lines: FROM qwen3.5:9b PARAMETER num_ctx 65536 2. Save this file exactly as Modelfile (make sure it has no .txt file extension) inside your active working directory. 3. Back in your terminal, build your new, superpower-backed model by running: ollama create qwen3.5-9b-64k -f Modelfile Verify it compiled correctly by typing ollama list. Your new qwen3.5-9b-64k model should show up right at the top of the list. Note: If you are tight on storage space, you can safely remove the original base model using ollama rm qwen3.5:9b since your new custom build handles the heavy lifting. Step 5: Launch Claude Code with Your Local Model Now it’s time to link them up. Navigate to the local coding project you want to work on using the cd command, and launch Claude Code pointed directly to your high-context local model: ollama launch claude-model qwen3.5-9b-64k Claude Code will initialize right inside your project directory. It will prompt a quick security safety check asking if you trust the project folder — confirm it, and you’re officially in. How to Verify It’s Working: To ensure Anthropic’s cloud isn’t secretly receiving your requests and billing your account, type the following command inside the Claude Code interface: /models You will see a matrix showing that all model slots — including Sonnet, Opus, and Haiku — are actively pointing back to your local qwen3.5-9b-64k model. Your local machine is now driving the entire developer environment. Real-World Performance If you want to track how your hardware is coping while you code, open up a separate, parallel terminal window and run: ollama ps This utility gives you a live look at your active local models and their exact VRAM/RAM footprints. On a mid-tier 16GB system, expect your resources to average roughly 45% CPU utilization and 55% GPU utilization during intensive generation cycles. Because it’s running locally, the model will take longer to “think” when executing complex repository sweeps. For instance, if you paste a massive GitHub URL repository and ask it to clone the codebase, map its entire architectural layout, find critical code flaws, and calculate deployment specifications, it can take up to 10 or 11 minutes of continuous processing. But the trade-off is unmatched: the output is incredibly thorough, entirely private, completely unlimited, and costs exactly $0 in API fees . Troubleshooting Common Windows Challenges Source: Image by the author. Even if it appears that you have every aspect set up as intended, running local AI tools on Windows can at times put a wrench in your workflow. If your terminal offers you trouble, then here are the most typical technical hurdles and specifically how to fix them: 1. “Command Not Found” (Even After Updating the Path) If you have meticulously added C:\Users\ \.local\bin to your environment variables but typing claude still throws an error, don't panic. Windows terminals do not automatically refresh their system environment map in active sessions. Fix: Simply close your current PowerShell window entirely and open a brand-new instance. If you are using an integrated terminal inside an IDE like VS Code, you will need to restart the entire IDE for the changes to take effect. 2. The Invisible .txt Extension Trap When creating your Modelfile, Windows File Explorer loves to be "helpful" by secretly saving it as Modelfile.txt while hiding known extensions from your view. If Ollama tells you it can't find or read the file during the compile step, this hidden extension is almost always the culprit. Fix: Open File Explorer, click on View (or Options depending on your Windows version), and check the box for File name extensions . If you see Modelfile.txt, right-click it, select rename, and delete the .txt entirely so it stands alone as just Modelfile. 3. Sluggish Performance and Intense System Lag If your entire machine locks up, freezes, or responds at a snail’s pace while Claude Code is processing a request, your system memory is likely hitting a hard bottleneck. This happens when your hardware is forced to offload model weights from your fast VRAM/RAM onto your slower local system drive. Fix: Close heavy background apps like Google Chrome tabs, Discord, or Docker containers before launching your local model. If you are on a strict 16GB RAM limit and resource contention persists, consider dropping the context limit in your Modelfile from 65536 down to 32768 (32K). This still gives you double the default capacity while significantly reducing the active memory footprint. 4. The “Connection Refused” Ollama Error If Claude Code throws an error saying it cannot establish a connection to your local backend, the Ollama background host daemon has either crashed or hasn’t initialized yet. Fix: Look at your Windows taskbar system tray (the small arrow icon in the bottom right corner) and verify that the Ollama llama icon is visible. If it isn’t there, search for Ollama in your Windows Start Menu and launch it manually to wake up the local server API before trying to run Claude Code again. Thanks for sticking around to the end. If you found these checkpoints helpful, a few claps go a long way. Let me know your thoughts regarding this article in the comments below. I am a tech writer and the founder of AnalystHQ, where I simplify data analytics and AI workflows. If you want to dive deeper into practical data skills, you can check out my complete playbook, the SQL & Excel Data Analyst Course . Claude Code Free with Local AI was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
Score: 20🌐 MovesMay 26, 2026https://pub.towardsai.net/claude-code-free-with-local-ai-ffbc92208006?source=rss----98111c9905da---4 - Google wants you to know that its AI Ultra plan is different from its AI Ultra plan
Hey Google, how about we change the name instead?
Score: 20🌐 MovesMay 26, 2026https://www.androidauthority.com/google-ai-ultra-plan-differences-3670856/ - Act quickly to get the Husqvarna 420iQ Robotic Mower while its on sale for $700 off
The Husqvarna 420iQ Robotic Mower is on sale at Amazon for $2,599.99, down from the normal price of $3,299.99. That's a 21% discount.
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- ChatGPT might be quietly rewarding people who know how to think clearly — these prompts can help
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- Claude, Author of the Humanitas
In the wee hours of Memorial Day, my friends and I stayed up past 4:30 AM California time to listen to the announcement of Pope Leo’s first encyclical, Magnifica Humanitas , on safeguarding the human person in the time of artificial intelligence. We were excited albeit sleepy, eagerly anticipating the event and upcoming essay by the world’s foremost religious authority on a question so central to our world. Still we were an odd audience for this presentation: none of us are practicing Catholics, and most of us didn’t really know what to expect. I thought Pope Leo’s own speech was good, and addressed the current moment in AI with some of the seriousness it deserves. I thought the other speeches, including by Chris Olah, were less impressive. But that’s okay, I’m not the target audience! A specific cardinal’s point struck me, however: Cardinal Parolin made much of a specific prepositional choice in the subtitle: “ sulla custodia della persona umana nel tempo dell’intelligenza artificiale, “ which the live translator translated to something like “on the safeguarding of the human person in the time of AI,” and not “ sull’intelligenza artificiale “ – “on AI.” This was supposed to be a big deal. “ In the time of AI” supposedly centers the human person in the theological narrative, while a mere first papal encyclical on AI focuses too much on the technology itself and not on human and societal reactions. A fascinating position! Though as my subsequent analysis will demonstrate, perhaps a more apt preposition here is “ by .” As in, the world’s first papal encyclical written in large part by AI. My article has the following claims, each of which I hope to convince you of: Significant fractions of the recent papal encyclical are written by AI. I provide multiple lines of evidence for this. We can corroborate the vibes and tonal indications with statistical evidence. Phrases and punctuation much more commonly used by AI are much more present in this papal encyclical than past encyclicals. The best commercially available AI detector, Pangram, notes that some paragraphs are between 40% and 100% AI, while most paragraphs appear to be 0% AI. This is unlikely to be a false positive: 0% of paragraphs in past encyclicals I backtested are registered as AI. Pangram in general has a very low false positive rate This is overall very unlikely to be a translation artifact (including AI translation). We again have multiple lines of evidence: All the most prominent signs of AI I observed in English are preserved verbatim in the Italian version, as well as in other translations. The Italian version of the current encyclical also gets flagged as AI by Pangram (actually more so than the English version), though I’m not aware of academic research or rigorous testing of Pangram’s service when applied to Italian) Backtesting AI translation of past encyclicals get 0% on Pangram The specific AI used is most likely Claude, judging by both textual and circumstantial evidence. Different sections of the encyclical have very different rates of apparent AI usage. This indicates to me that some cardinals used AI assistance for this encyclical and many (probably including Pope Leo himself) don’t. Each individual piece of evidence might be explained away, but the consilience of evidence across multiple angles and sources is in my opinion very hard to dismiss collectively. Significant fractions of the recent papal encyclical are written by AI I was initially very excited to read Pope Leo’s first encyclical, a long treatise on maintaining humanity in the age of AI. The intersection between AI and societal response is one of my greatest intellectual and personal interests, and it’s both exciting and a relief for the world’s foremost religious authority to share a substantial interest in my personal and career obsessions. Nonetheless – as I kept reading – certain lines jumped out at me as too smooth, too triadic, too… inhuman : “Technology has the power to heal, connect, educate and protect our common home; but it can also divide, exclude and generate new forms of injustice. In the abstract, technology in and of itself is not a solution to humanity’s problems, just as it is not inherently evil. In practice, however, technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate and use it.” “We must, then, avoid the “Babel syndrome,” namely the idolatry of profit that sacrifices the weak, a uniformity that neutralizes differences, and the pretense that a single language — even a digital one — can translate everything, including the mystery of the person, into data and performance. The risk of dehumanization — of building a future that excludes God and reduces the other to a means — is an ancient and ever-new temptation that today takes on a technical guise.” “A dialogue with such kinds of knowledge does not diminish the power of the Gospel. On the contrary, it makes it possible to identify with greater clarity what genuinely fosters the lives of individuals and communities.” “give stable form to this insight at the ecclesial and international levels, while bearing in mind the growing gap between rich and poor countries and the need for policies that genuinely promote more humane living conditions for all.” “We cannot be satisfied with merely calling for the moralization of machines — the so-called “alignment” of AI with human values — without also having the courage to insist on a further condition: the possibility of openly discussing the ethical frameworks involved and subjecting them to shared standards of social justice” I read AI-generated text as part of my job regularly, and believe I have acquired a very good intuition for discerning AI-generated text from those by humans, including in formal writing (both academic and otherwise). Still, any individual phrase that seems AI-generated can be a false positive on my end, the result of an oversensitive nose for AI 1 . However, the sheer density of these phrases and overall tone in specific paragraphs seem implausibly all a random artifact. Still, I can definitely be wrong here, and you should not believe my gut intuitions or judgments of vibes on authority (“Trust me bro”). Intuitions, self-proclaimed expert judgments, and loose verbal reasoning can be a good starting point for an investigation, but if we want any confidence in our conclusions, we need to investigate further and more systematically. Statistical Evidence and Tells Three common and well-known tells in AI writing — sometimes genuinely deployed by humans but nowhere their profligate use by AI — are the regularity of em-dashes, the high frequency of specific words like “genuinely”, and the tendency to repeatedly invoke tricolons. Let’s examine each of them in turn: Em-dashes The em-dash (“—”) is punctuation that’s by far most strongly associated with AI. It is also used 127 times in Magnifica Humanitas , much more than previous encyclicals. Magnifica Humanitas: 127 times em-dash, 6 times en-dash (–), the latter all in citations. 2 Dilexit Nos (2024) : 0 times em-dash, 26 en-dashes, including 2 in citations. Comparatively long document. 3 Laudate Deum (2023): 0 times em-dash, 12 times en-dashes. Much shorter. Also not officially an encyclical. Fratelli Tutti (2020): 0 times em-dash, 46 en-dashes, of which maybe 5-10 are in quotes or citations. Note that this is 50% longer than Magnifica Humanitas. Laudato Si’ (2016) : 0 times em-dash, 25 times en-dash, of which maybe 10 are in citations or quotes (the piece overall appears to have many quotes). Similar length to Magnifica Humanitas Lumen Fidei (2013): 26 times em-dash, 0 times en-dash. Some em-dashes in citations. Note that this comparison actually understates the weirdness of the em-dashes in Magnifica Humanitas. For example, in Lumen Fidei , many of the em-dashes function similarly to speech colons in standard English. A typical use looks like What was handed down by the apostles — as the Second Vatican Council states — “comprises everything that serves to make the people of God live their lives in holiness and increase their faith. In this way the Church, in her doctrine, life and worship, perpetuates and transmits to every generation all that she herself is, all that she believes.” Using em-dashes as speech colon replacements is moderately common in formal (human) English writing, but essentially absent in LLM-English. I also did not notice em-dashes used this way in Magnifica Humanitas (though with 127 instances, it was annoying to check all of them!) “Genuinely” “Genuinely” is a phrase repeatedly used by Anthropic’s model Claude. It is extremely obvious to anybody who regularly uses it. It’s gotten so bad that in leaked system prompts , Anthropic attempted to explicitly forbid Claude to use that word! 1.4 Tone & Formatting [...] Claude avoids saying “genuinely”, “honestly”, or “straightforward”. 4 As far as I could tell, this injunction does not and did not work. Indeed, Anthropic’s own “ Claude Constitution ”, which many people believe to be substantially AI-assisted, used the phrase “genuinely” 33 times and genuine overall 50 times (inclusive). How often is the phrase “genuinely” used in Magnifica Humanitas? Less so than in Anthropic documents, but substantially more than past papal writings. Specifically “genuinely” was used 9 times and “genuine” overall (inclusive) 22 times in yesterday’s encyclical, compared to 0 and 5 times, respectively, in Dilexit Nos, which is of similar length. Across a number of other encyclicals I scanned, the highest occurrences were 3 and 10, respectively. These tells are all statistical. Any individual instance of “genuine(ly)” is plausibly a result of normal human communicative intent that is, well, genuine. But the sheer frequency of these occurrences, vastly out of accord with prior norms and normal human speech, is strongly suggestive of synthetic origin. Is this due to subject matter? An obvious rejoinder you might have is that word choices in essays are naturally not independent of subject matter. And it sure seems like an encyclical on AI might meditate more about genuineness more than other encyclicals! For example, an essay on AI deepfakes might be much more concerned about what makes a video “genuinely human” than an essay on climate change. To investigate this hypothesis, I dived specifically into each use of genuinely in this encyclical: [Par 23] “A dialogue with such kinds of knowledge does not diminish the power of the Gospel. On the contrary, it makes it possible to identify with greater clarity what genuinely fosters the lives of individuals and communities. Following this perspective, Pope Francis [...] recognizes the importance of listening to scientific research and of encouraging a serious and honest debate among experts while welcoming a diversity of opinions.” “Genuinely” does not seem critical here, nor specific to questions of AI and authenticity. [Par 35] The establishment of the Pontifical Commission Iustitia et Pax should also be seen in this light as an attempt to give stable form to this insight at the ecclesial and international levels, while bearing in mind the growing gap between rich and poor countries and the need for policies that genuinely promote more humane living conditions for all. Also not critical here. [Par 40] In his social Encyclical Caritas in Veritate, Pope Benedict XVI sought to reassess and expand the concept of development presented in Populorum Progressio, interpreting it in light of globalization. He noted that such development should translate into “real growth, of benefit to everyone and genuinely sustainable.” [42] Appears to be in a quote, so will give it a pass. 5 [Par 57] Along with a greater awareness of the value of every human person and their rights, recognition of minority rights has also grown. Yet, there is still a long way to go to ensure that the rights of a great many, namely women, are equally and genuinely guaranteed throughout the world. Again, does not seem like “genuinely” was endogenously related to the subject matter [Par 100] The artificial imitation of positive human communication — words of advice, empathy, friendship and even love — can be engaging and at times genuinely helpful. However, for less discerning users, it can also be misleading, creating the illusion of a relationship with a real personal subject. When words are simulated, they do not build genuine relationships, but only their appearance. The artificial imitation of care or support can become particularly risky when it enters contexts where real relationships and emotional bonds are lacking. Here, the danger is not so much that a person may believe they are communicating with another person, but rather that they may gradually lose the very desire to form genuine human connections. …you get the idea. 6 Indeed, of all 9 instances of “genuinely” in the encyclical, only the last use (“When people come to believe that nothing is genuinely true and that principles are hollow words, then the fuse in their hearts is lit for new eruptions of intolerance and aggression.”) seem semantically critical. If we drop that and the Pope Benedict quote we’re left with 7/9 suspicious uses. Again, to be clear any individual instance is plausibly normal, authentic, genuine. However the statistical pattern of the repeated invocations is quite suspicious! Is this just a personality quirk of Pope Leo XIV specifically? Another possibility you might have is that maybe this is just a personality/stylistic quirk of Pope Leo XIV specifically? Maybe he just genuinely likes the word? Lord knows I too have odd personality quirks in writing, some of which have an unfortunate resemblance to AI . Ultimately, I think this is plausible but unlikely. First of all, popes don’t typically draft the text of their own encyclicals that much. So it’s unlikely that stylistic quirks as specific as adverbial usage will bleed out to the final drafts as much. In contrast, I’m much more open to higher level constructs like the imagery, themes, or favorite Bible passages being much more prominent in some pope’s encyclicals than others. Further, the specific phrases used are often next to other suspicious “AI tells” (more on that later). Unfortunately, I don’t have easy access to many (pre-papacy) writings by Pope Leo to test against this alternative hypothesis. However, I did find Chapter 2 (“The Authority of the Local Prior”) of his 1987 PhD thesis here . In 14 pages (roughly the size of the post you’re reading), the future Pope Leo’s chapter has no uses of “genuine” or “genuinely,” and 0 em-dashes in his own prose. 7 (I welcome extensions of my analysis by people with access to the full thesis in print). Tricolon density A common mark of LLM writing is the repeated invocation of tricolons: a series of three parallel words, phrases, or clauses used for rhetorical effect. I noticed quite a few invocations of the tricolons in Magnifica Humanitas. It was especially notable in sections that otherwise had other tells of AI. Unfortunately, unlike “genuinely” or em-dashes, this is harder to directly observe or baseline, as I can’t use the automatic “find” feature on chrome, it’s annoying to count by hand, and there are numerous edge-cases. Nonetheless, I attempted to use my AI Agent Claude Code ( Claude Opus 4.7 1M XHigh) to give it a good college try, testing Magnifica Humanitas against 3 encyclicals by Francis, 2 by Benedict, 1 jointly by Benedict and Francis and 1 by Leo XIII (who wrote Rerum Novarum , which the current encyclical on AI is supposedly strongly based on). Caption: Note the easy and natural use of em-dashes above. This is how the AIs naturally speak! I think this is partial confirmation of my hypothesis. Strict tricolons seem noticeably more prominent in pope Leo XIV’s writings than that of past popes we tested against. Unfortunately (for my hypothesis) there is also substantial variation in the encyclicals authored/commissioned by previous popes. In particular, tricolons are much more common in writings by Benedict than by Francis. So this simple test is suggestive but does not rule out normal human variation. Further, the LLM scan is a rough estimate. There’s inherent subjectivity in a question of triadic markers (unlike more direct vocabulary or punctuation tells). I welcome replication of my attempts here, either via a different AI agent methodology, or (preferably) someone more patient than me willing to manually count and verify this. Do we have other sources of evidence to look at? Yes, we can use automated AI detectors, specifically Pangram. Pangram analysis Pangram is by far the best commercially available AI detector . It is much better than other AI detectors, so much so that other ones are almost useless in comparison. In particular, Pangram optimizes very hard for getting a false positive rate of nearly zero, while being more okay with false negatives. This means that if you see text online that Pangram flags as AI, you should have very high confidence that it’s AI. In contrast, if you have some text that Pangram flags as 100% human, you should still be appropriately skeptical, especially if you’re otherwise suspicious. So how did yesterday’s encyclical do? When I pasted the first twenty paragraphs of the encyclical in 8 , Pangram flags 11% of it as AI. In particular, Pangram is very suspicious of Paragraphs 7-8. For what it’s worth, I too was rather suspicious of this section. “It was an impressive feat: a single language, a single technology, a single direction.” sounded just a bit too neat to me. Spot-checking different sections of the encyclical, we see a repeated pattern. Some sections register as essentially 0% AI, while others seem much more AI-y. This indicates to me that some cardinals who contributed to the encyclical used AI assistance heavily and most (probably including Pope Leo himself) did not. More on this later. Comparison to other encyclicals You might naturally be skeptical of Pangram’s analysis here. After all, maybe you haven’t heard of Pangram (or myself) until today. And besides, Pangram’s likely trained on internet data and its main use case is detecting AI in blog posts and social media posts and movie reviews and academic papers and so forth. What evidence do we have that it’s suited for a task as off-distribution as papal encyclicals? Maybe the detector’s just befuddled by unusually pious tokens? To test this hypothesis, I ran Pangram on the previous 4 encyclicals . The first 20 paragraphs on all of them register as 100% human, all with high confidence: Pangram on some past encyclicals Pangram on some past encyclicals Pangram on some past encyclicals Pangram on some past encyclicals Pangram on some past encyclicals Pangram on some past encyclicals Pangram on some past encyclicals Pangram on some past encyclicals I also tested writings by Pope Benedict and John Paul in case Pope Francis had an unusually human touch. And against encyclicals by Pope Leos XIII and XII in case Pangram is prejudiced against Leos. All 100% human, as expected. Comparison to Pope Leo XIV’s speech I also tested it against a transcript of Pope Leo’s speech announcing yesterday’s encyclical. 100% Human on Pangram. This is evidence that Pope Leo himself and/or his primary speechwriter does not use AI to draft his speeches. (Incidentally, “genuine(ly)” appears 0 times in the transcript. Though em-dashes appear three times). Sidebar: Pangram has a very low false positive rate in general I think the encyclical evidence specifically should be quite convincing. But separately, I also strongly believe Pangram has a very low false positive rate in general. I observed this both in academic research and my own tests: I’ve tested writings that I’m very confident is not AI (e.g. writings by myself, or from before 2021) dozens if not hundreds of times against Pangram, and repeatedly gotten 100% Human. This indicates to me that their advertised very low false positive rate is real. In contrast, the false negative rate is much higher: Sometimes I’d ask an AI to read an outline by me and generate a draft, as a test. Pangram only catches that sometimes , though my impression is that they’ve gotten better in the last few months. So you should generally trust that text that Pangram flags as AI is probably AI, while continuing to maintain some healthy suspicion of text that Pangram flags as not AI. Probably not a translation artifact One potential mitigating factor is that this is a translation artifact: (human) senior church officials wrote the original encyclical in their native language (maybe Italian) and the translators , not the writers, were lazy and used substantially AI assistance in translating to English. I cannot rule this hypothesis out but currently think it’s very unlikely. The same signs of AI I observe in English are essentially preserved verbatim in the Italian version If the strong AI signs I’ve observed (“tells”) are a result of lazy translators to English, we should expect them to look different in other languages, especially the source language. To test this, I gave all the tells I noticed in the English encyclical as well as the Italian version of the encyclical to two software agents – Claude Opus 4.7 and ChatGPT 5.5 Pro, arguably the strongest commercially available language models out there – to see whether the tells were preserved in Italian. Both agents believe the tells were preserved verbatim (See Claude screenshot below): That said, I don’t speak Italian myself, and cannot personally verify the results. I welcome replications. Suspicious readers who speak Italian, and especially Italian native speakers who are familiar enough with how AI sounds in Italian, should try to verify the work for themselves! The Italian version of the current encyclical also gets flagged as AI by Pangram If the English Pangram results are due to overly technophiliac or lazy translators of Italian to English, we should expect that the original Italian results will get 0% while the English translations gets flagged. We do not observe this (first twenty paragraphs again). Instead , the Italian flagged sections appear to be a superset of the sections flagged in English. This would naively indicate that more, not less, of the Italian sections were drafted by AI. (See further analysis by Daniel Filan) That said, I don’t know how accurate Italian Pangram is. All the academic research I am aware of on Pangram’s accuracy (and my own experiments) are in English. Backtesting AI translation of past encyclicals get 0% on Pangram Another angle on the translation artifact hypothesis: Suppose you 100% organically human-write an article and then use AI to translate it. Does this even get flagged by Pangram? As far as I can tell, the answer is no! (H/T Daniel Filan for this methodology) I took a random excerpt of Fratelli Tutti in Italian and asked 3 leading frontier models (Gemini 3.1, ChatGPT 5.5, Claude 4.7) to translate it to English, with web search off. Each translation is different from the official Vatican translation (and from each other). Pangram reads all three texts as 100% human. ( Gemini results , Claude results , ChatGPT results ) This indicates to me further evidence that any Pangram-detected signs of AI in Magnifica Humanitas come from stylistic features that are present across languages, rather than artifacts plausibly introduced by AIs in translation. Share The specific AI used is likely Claude The AI I use the most often for work is Anthropic’s Claude. I have a decent sense of its underlying rhythm, how it argues, and its favorite vocabulary and syntactic choices. 9 I believe I identified the same voice of Claude in the recent papal encyclical about safeguarding the human person in the age of artificial intelligence. Somewhat ironic, considering. Unfortunately, my primary reasons for believing this are somewhat idiosyncratic and inscrutable, and thus more likely to be wrong. And the “Claude authorship” hypothesis is of course overall less important to nail than the “AI authorship” hypothesis, all things considered. Still, I want to offer some textual evidence favoring Claude over other models 10 . Textual The aforementioned “genuinely” strongly bears the fingerprints of Claude. It is very much the house style of Claude/Anthropic and regularly used in both internal and external Anthropic communications about Claude’s nature and in Claude’s repeated outputs. To be honest, I see “genuinely” so much these days that the word no longer feels like a real word to me and I’ve completely lost the ability to spell the word correctly by myself, never mind appreciating it in context. Maybe there’s a metaphor in there about authenticity in the age of AI? Nah, I’m probably overthinking it. Regardless, the repeated uses of genuinely in Magnifica Humanitas is strongly suggestive of Claude-specific fingerprints. Against ChatGPT Applying the same analysis against ChatGPT fingerprints gives us a negative result. The words that I most associate with ChatGPT (“ delve ”, “meticulous,” “tapestry”, “ goblins ”) over other LLMs all show up zero times in the English encyclical. This is moderate evidence against significant ChatGPT usage. I don’t know enough about the tells that are specific to other models (Gemini, Grok, Mistral, DeepSeek, Kimi , etc). I welcome replications! Different sections of the encyclical have very different rates of apparent AI usage Significant fractions of the text in both English and Italian are flagged as AI. This varies significantly from section to section and paragraph to paragraph. Some paragraphs in Pangram, and also by visual inspection, are clearly AI, while others seem clearly not AI. My understanding is that popes don’t usually draft the majority of the text of their encyclicals themselves. Between these two facts, this indicates to me that some cardinals heavily used AI assistance for this encyclical and many (probably including Pope Leo himself) didn’t. My tentative hypothesis is that Pope Leo does not approve of the AI usage in encyclicals, and plausibly was not even aware of significant AI usage in his own encyclical! Quite unfortunate if true. Conclusion I attempted to provide a number of different angles and evidence for the conclusion that the papal encyclical on safeguarding the human person in the age of AI is actually an article in large part neither on nor in AI, but by AI. Any individual method might be flawed, but I believe the consilience of evidence is very strongly suggestive, perhaps even overwhelming. Nonetheless, I welcome good-faith debate and corrections. Please feel free to comment with contrary evidence and replications. We’re soon entering a time of unprecedented danger in the world . Like children with flamethrowers, or Bronze Age peasants attempting to build the Tower of Babel, humanity is messing with forces we cannot hope to understand or control. In this new age of AI, getting provenance genuinuely right isn’t just a question of human authenticity — it’s a matter of life and death. 1 Though in previous attempts to validate my intuition systematically, I have a substantially higher false negative rate than false positive rate, suggesting I’m not sensitive enough . 2 Technically 6 times, but all 6 in citations (eg “ cf Neh 2–6 , [123] Cf. Dicastery for the Doctrine of the Faith – Dicastery for Culture and Education”) 3 Unedited AI texts use em-dashes much more often than en-dashes. The former is much more directly a sign of AI than the latter. However, it’s common for people hiding AI writing to change em-dashes to en-dashes. Additionally, the specific differences between em-dashes and en-dashes could just be random variation of specific stylistic preferences/trends between people. Thus, we should probably count em-dashes and en-dashes together in the analysis, though em-dashes over en-dashes is still some (nonzero) evidence. 4 I asked Claude to review this section. It says the injunction to avoid “genuinely” is no longer in its system prompt as of Opus 4.7 (and has no direct evidence of past prompts), though it believes the previous/github report is credible enough for Opus 4.6. 5 This is not strictly zero information. Given Claude’s demonstrated predilection for genuinely, we can imagine this specific passage was chosen by Claude to quote. 6 Interestingly, here the instances of “genuine” (“genuine relationships”, “genuine human connections”) seemed to carry actual semantic content, while “genuinely helpful” did not. So despite its placement in the rest of the paragraph, I don’t think “genuinely” is semantically relevant here. 7 If you’re very literal about it, there are 2 em-dashes in the chapter. One of them is in a large quote-block by someone else. The other is in the footnote “Robert F. Prevost, O.S.A. — Canon lawyer working in Chulucanas, Peru.” Neither invocations are semantic in the main text, and the latter likely added by an editor. 8 The whole encyclical is too long for Pangram. 9 I did not use AI to draft or write any of the text in this post, including for sentences deliberately engineered to sound like AI. 10 Note that I’m only moderately confident in the presence, and to a lesser degree, the substantial contribution, of Claude’s fingerprints on the recent encyclical. I can’t rule out that other AIs were also separately involved. I’m also only moderately confident in my conclusion that it’s Claude: all the AIs are kinda similar to each other, so differentiating between them is much harder than detecting the presence of AI at all. Discuss
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