---
title: "Daily paper"
description: "Browse the daily paper calendar, open past picks, and read the current paper with a short learning note."
canonical_url: "https://fanout.sh/daily"
md_url: "https://fanout.sh/daily.md"
access: "public"
---

# Daily paper

Browse the daily paper calendar, open past picks, and read the current paper with a short learning note.

## Paper archive

### [Segment Anything](https://fanout.sh/daily/2026-08-25-segment-anything.md)

Segment Anything asks whether one vision model can turn a point, box, or rough mask into the pixels of a reasonable object, even on unfamiliar images. Read it to see how one reusable image embedding, a lightweight prompt decoder, ambiguity-aware mask candidates, and a model-in-the-loop data engine make segmentation feel like an interface rather than a fixed label set.

- Authors: Kirillov et al.
- Venue: ICCV 2023
- Track: AI Research
- Difficulty: Easy
- Reading time: 35 min

### [Large-scale cluster management at Google with Borg](https://fanout.sh/daily/2026-08-24-google-borg-cluster-management.md)

Borg asks how one fleet can run latency-sensitive services and opportunistic batch work without wasting the space between their peaks. Read it to see how cells, jobs, tasks, priorities, limits, reservations, placement scoring, and a continuously repaired control plane turn heterogeneous machines into shared infrastructure.

- Authors: Verma et al.
- Venue: EuroSys 2015
- Track: Systems
- Difficulty: Easy
- Reading time: 40 min

### [Learning Transferable Visual Models From Natural Language Supervision](https://fanout.sh/daily/2026-08-23-clip-natural-language-supervision.md)

CLIP asks how an image model can recognize categories described in words without training a new classifier for every task. Read it to see how two encoders turn a batch into an all-pairs similarity matrix, how the true image-text pairs teach a shared embedding space, and why prompts make zero-shot recognition flexible but not automatically reliable.

- Authors: Radford et al.
- Venue: ICML 2021
- Track: AI Research
- Difficulty: Easy
- Reading time: 35 min

### [Development of the Domain Name System](https://fanout.sh/daily/2026-08-22-development-domain-name-system.md)

This paper asks how the Internet can keep one usable naming system without making every machine download one global host file. Read it to see how DNS cuts a name tree into delegated zones, follows referrals toward the server responsible for one branch, and uses time-limited caching to make repeated lookups local.

- Authors: Mockapetris and Dunlap
- Venue: SIGCOMM 1988
- Track: Systems
- Difficulty: Very Easy
- Reading time: 30 min

### [End-to-End Arguments in System Design](https://fanout.sh/daily/2026-08-21-end-to-end-arguments.md)

This paper asks where a system should place reliability, delivery, security, and duplicate-suppression guarantees. Read it to see why a lower layer can make failures rarer yet still cannot certify the application result, and why the final check belongs at the endpoints that understand what success means.

- Authors: Saltzer, Reed, and Clark
- Venue: ACM TOCS, 1984
- Track: Systems
- Difficulty: Easy
- Reading time: 30 min

### [Toolformer: Language Models Can Teach Themselves to Use Tools](https://fanout.sh/daily/2026-08-20-toolformer.md)

Toolformer asks how a language model can learn when and how to call external tools without a large hand-labeled tool-use dataset. Read it to see how the model proposes calls inside ordinary text, executes them, and keeps only those whose returned result lowers continuation loss enough to become a training example.

- Authors: Schick et al.
- Venue: NeurIPS 2023
- Track: AI Research
- Difficulty: Easy
- Reading time: 40 min

### [An Industrial-Strength Audio Search Algorithm](https://fanout.sh/daily/2026-08-19-shazam-audio-search.md)

This is the paper behind Shazam: how a phone can name a song from ten noisy seconds in a pub. Read it to see how a track is reduced to a constellation of spectrogram peaks, how pairing peaks into 32-bit hashes makes lookup about ten thousand times faster, and why a match is declared only when the surviving hashes agree on one time offset.

- Authors: Avery Li-Chun Wang
- Venue: ISMIR 2003
- Track: Systems
- Difficulty: Easy
- Reading time: 30 min

### [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://fanout.sh/daily/2026-08-18-chain-of-thought.md)

This paper asks why showing a language model a few worked examples of step-by-step reasoning unlocks problems that direct prompting cannot touch. Read it to see how eight hand-written exemplars lifted PaLM 540B from 17.9% to 56.9% on grade-school math, why meaningless filler tokens explain none of the gain, and why the same prompt actively hurts smaller models.

- Authors: Wei, Wang, Schuurmans, Bosma, Ichter, Xia, Chi, Le, and Zhou
- Venue: NeurIPS 2022
- Track: AI Research
- Difficulty: Easy
- Reading time: 30 min

### [Space/Time Trade-offs in Hash Coding with Allowable Errors](https://fanout.sh/daily/2026-08-17-bloom-filter.md)

Bloom's paper asks how a compact in-memory screen can reject missing keys before an expensive exact lookup. Read it to see why one zero bit proves absence, why all-one evidence means only possibly present, and how shared hash positions trade memory for a controlled false-positive path.

- Authors: Burton H. Bloom
- Venue: Communications of the ACM, 1970
- Track: Systems
- Difficulty: Easy
- Reading time: 30 min

### [Tor: The Second-Generation Onion Router](https://fanout.sh/daily/2026-08-16-tor-onion-router.md)

This paper asks how a browser can cross several volunteer relays without handing any one relay the complete route. Read it to see how the client extends a circuit one hop at a time, wraps each fixed-size cell in a different session key for every relay, and why those layers still cannot defeat an observer who can correlate traffic at both ends.

- Authors: Dingledine, Mathewson, and Syverson
- Venue: USENIX Security 2004
- Track: Systems
- Difficulty: Easy
- Reading time: 35 min

### [Mastering the game of Go with deep neural networks and tree search](https://fanout.sh/daily/2026-08-15-alphago-neural-tree-search.md)

This paper asks how a computer can choose a strong Go move when the game tree is far too large to enumerate. Read it to see how a learned policy concentrates search on promising moves, how a value network estimates who is ahead before a game ends, and how Monte Carlo tree search turns those estimates into professional-level play under a large compute budget.

- Authors: Silver et al.
- Venue: Nature 2016
- Track: AI Research
- Difficulty: Easy
- Reading time: 35 min

### [Bitcoin: A Peer-to-Peer Electronic Cash System](https://fanout.sh/daily/2026-08-14-bitcoin-proof-of-work.md)

This paper asks how two people can agree that a digital payment happened first without asking a central mint to keep the ledger. Read it to see why signatures prove ownership but not uniqueness, how hash-linked proof of work makes one public history costly to rewrite, and why every confirmation still depends on an honest-compute majority assumption.

- Authors: Satoshi Nakamoto
- Venue: Bitcoin white paper, 2008
- Track: Systems
- Difficulty: Easy
- Reading time: 35 min

### [Congestion Avoidance and Control](https://fanout.sh/daily/2026-08-13-congestion-avoidance-control.md)

This paper asks how TCP can keep a busy network useful when delay, packet loss, and retransmission tempt every sender to add even more traffic. Read it to see how acknowledgements become a clock, how slow start fills an empty path without a full-window burst, and why a congestion signal should make the sender retreat quickly before probing capacity again.

- Authors: Van Jacobson
- Venue: SIGCOMM 1988
- Track: Systems
- Difficulty: Easy
- Reading time: 35 min

### [Stealing Reasoning Traces from Proprietary LLM APIs](https://fanout.sh/daily/2026-08-12-stolen-reasoning-traces.md)

This paper asks what happens when an API gives encrypted reasoning state to the client and later accepts that opaque state again. Read it to see why confidentiality and integrity do not automatically bind a reasoning block to one user, session, or model, how the authors tested compatible weaker decoders, and what the resulting privacy evidence means after responsible disclosure.

- Authors: Panfilov, Schmotz, Shumailov, Beurer-Kellner, Schaeffer, Prabhu, Geiping, and Andriushchenko
- Venue: arXiv, 2026
- Track: AI Research
- Difficulty: Medium
- Reading time: 35 min

### [The PageRank Citation Ranking: Bringing Order to the Web](https://fanout.sh/daily/2026-08-11-pagerank.md)

PageRank asks how a search engine can estimate which web pages matter before it even sees a query. Read it to see how every page divides its authority among outgoing links, how repeated link-weight transfer settles into a global ranking, and why teleportation, incomplete crawls, and manipulation keep that score from being a synonym for truth.

- Authors: Page, Brin, Motwani, and Winograd
- Venue: Stanford InfoLab, 1999
- Track: Systems
- Difficulty: Easy
- Reading time: 35 min

### [ReAct: Synergizing Reasoning and Acting in Language Models](https://fanout.sh/daily/2026-08-10-react-reasoning-acting.md)

ReAct gives language-model agents a simple working rhythm: reason about the current situation, take a bounded action, observe what the environment returns, and revise the next move. Read it for the durable idea behind many tool-using agent harnesses and for an honest look at why better grounding can still fail when tools return weak evidence or the agent gets stuck in a loop.

- Authors: Yao, Zhao, Yu, Du, Shafran, Narasimhan, and Cao
- Venue: ICLR 2023
- Track: AI Research
- Difficulty: Easy
- Reading time: 35 min

### [Ring Attention with Blockwise Transformers for Near-Infinite Context](https://fanout.sh/daily/2026-08-09-ring-attention.md)

Ring Attention asks how exact Transformer context can grow across devices without gathering the whole sequence on each one. Read it to see how stationary query blocks, circulating key-value blocks, and compute-transfer overlap turn a device ring into aggregate context capacity while keeping per-host activation memory independent of total sequence length.

- Authors: Liu, Zaharia, and Abbeel
- Venue: ICLR 2024
- Track: Systems
- Difficulty: Hard
- Reading time: 50 min

### [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://fanout.sh/daily/2026-08-08-flashattention-2.md)

FlashAttention-2 asks why an exact, I/O-aware attention kernel can still leave much of an A100 idle. Read it to see how reducing non-matmul work, parallelizing along sequence tiles, and giving each warp disjoint query and output rows improves GPU utilization without changing the attention result.

- Authors: Tri Dao
- Venue: ICLR 2024
- Track: Inference
- Difficulty: Hard
- Reading time: 50 min

### [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://fanout.sh/daily/2026-08-07-mamba-selective-state-spaces.md)

Mamba asks how a recurrent model with a fixed-size state can decide what information deserves to survive. Read it to see how input-dependent state-space parameters create content-aware persistence and reset, and how a fused parallel scan makes the resulting time-varying recurrence practical on GPUs.

- Authors: Gu and Dao
- Venue: COLM 2024
- Track: AI Research
- Difficulty: Medium
- Reading time: 55 min

### [Root Mean Square Layer Normalization](https://fanout.sh/daily/2026-08-06-rmsnorm.md)

RMSNorm asks whether LayerNorm needs to subtract the mean as well as control activation scale. Read it to see how dividing by a vector's root mean square preserves global rescaling invariance with less normalization work, while deliberately giving up re-centering invariance.

- Authors: Zhang and Sennrich
- Venue: NeurIPS 2019
- Track: AI Research
- Difficulty: Medium
- Reading time: 35 min

### [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://fanout.sh/daily/2026-08-05-zero-memory-optimization.md)

ZeRO asks why adding data-parallel workers does not make a model easier to fit when every worker keeps the same training state. Read it to see how partitioning optimizer states, gradients, and parameters turns aggregate cluster memory into usable model capacity while preserving the data-parallel computation.

- Authors: Rajbhandari et al.
- Venue: SC 2020
- Track: Systems
- Difficulty: Medium
- Reading time: 50 min

### [Orca: A Distributed Serving System for Transformer-Based Generative Models](https://fanout.sh/daily/2026-08-04-orca-continuous-batching.md)

Orca moves LLM serving from fixed request batches to iteration-level scheduling. Read it to see how a scheduler can reconsider the active batch after every generated token, and how selective batching preserves efficient shared GPU work even when requests have different sequence lengths and lifetimes.

- Authors: Yu et al.
- Venue: OSDI 2022
- Track: Inference
- Difficulty: Easy
- Reading time: 45 min

### [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://fanout.sh/daily/2026-08-03-grouped-query-attention.md)

Grouped-query attention finds a practical middle ground between multi-head attention's quality and multi-query attention's decoding speed. Read it to see how sharing key-value heads within groups shrinks the KV cache, and how an existing multi-head checkpoint can be converted with only a small amount of additional pretraining.

- Authors: Ainslie et al.
- Venue: EMNLP 2023
- Track: Inference
- Difficulty: Medium
- Reading time: 35 min

### [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://fanout.sh/daily/2026-08-02-deepseekmath-grpo.md)

DeepSeekMath shows how targeted data and reinforcement learning can turn a compact open model into a strong mathematical reasoner. Read it for the data-selection pipeline behind its 120 billion math tokens and for GRPO, a PPO variant that removes the separate critic model by learning from relative rewards within groups of sampled answers.

- Authors: Shao et al.
- Venue: arXiv 2024
- Track: ML Math
- Difficulty: Hard
- Reading time: 55 min

### [Proximal Policy Optimization Algorithms](https://fanout.sh/daily/2026-08-01-proximal-policy-optimization.md)

Proximal Policy Optimization shows how to reuse on-policy experience for several minibatch updates without letting the policy move too far in one step. Read it to understand the clipped surrogate objective behind a practical reinforcement-learning algorithm that later became central to RLHF pipelines.

- Authors: Schulman et al.
- Venue: arXiv 2017
- Track: ML Math
- Difficulty: Hard
- Reading time: 55 min

### [Training Language Models to Follow Instructions with Human Feedback](https://fanout.sh/daily/2026-07-31-instructgpt.md)

InstructGPT shows why a larger language model is not automatically better at following a user's intent. Read it to see how demonstrations, preference rankings, a learned reward model, and reinforcement learning turn human judgments into a practical post-training pipeline for more helpful instruction following.

- Authors: Ouyang et al.
- Venue: NeurIPS 2022
- Track: AI Research
- Difficulty: Easy
- Reading time: 45 min

### [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://fanout.sh/daily/2026-07-30-switch-transformers.md)

Switch Transformers shows how to grow a model's capacity without activating every parameter for every token. Read it to see how top-1 expert routing, load balancing, and expert capacity turn mixture-of-experts into a simpler scaling recipe while keeping per-token computation roughly constant.

- Authors: Fedus, Zoph, and Shazeer
- Venue: JMLR 2022
- Track: AI Research
- Difficulty: Medium
- Reading time: 50 min

### [Distilling the Knowledge in a Neural Network](https://fanout.sh/daily/2026-07-29-knowledge-distillation.md)

Knowledge distillation turns an expensive ensemble or large teacher into a smaller, deployable student. Read it to see why the full pattern of class probabilities carries more useful structure than hard labels alone, and how temperature-scaled soft targets transfer that structure during training.

- Authors: Hinton, Vinyals, and Dean
- Venue: NIPS 2014 Deep Learning Workshop
- Track: AI Research
- Difficulty: Easy
- Reading time: 30 min

### [The Tail at Scale](https://fanout.sh/daily/2026-07-28-tail-at-scale.md)

The Tail at Scale explains why rare slowdowns become routine user-visible delays when one request fans out across many machines. Read it for a practical systems lesson: large services need techniques that tolerate latency variability, not only faster average components.

- Authors: Dean and Barroso
- Venue: Communications of the ACM 2013
- Track: Systems
- Difficulty: Easy
- Reading time: 30 min

### [Fast Inference from Transformers via Speculative Decoding](https://fanout.sh/daily/2026-07-27-speculative-decoding.md)

Speculative decoding makes autoregressive generation faster without changing the target model's output distribution. Read it to see how a small draft model, parallel verification, and a corrected sampling rule turn extra compute into lower decoding latency without retraining the models.

- Authors: Leviathan, Kalman, and Matias
- Venue: ICML 2023
- Track: Inference
- Difficulty: Medium
- Reading time: 40 min

### [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://fanout.sh/daily/2026-07-26-retrieval-augmented-generation.md)

Retrieval-Augmented Generation gives a language model an explicit, updateable memory outside its parameters. Read it to see how a neural retriever and a sequence generator work together, and why grounding generation in retrieved evidence can improve factual, knowledge-intensive answers.

- Authors: Lewis et al.
- Venue: NeurIPS 2020
- Track: AI Research
- Difficulty: Easy
- Reading time: 40 min

### [Denoising Diffusion Probabilistic Models](https://fanout.sh/daily/2026-07-25-denoising-diffusion-probabilistic-models.md)

Denoising Diffusion Probabilistic Models turns generation into a sequence of small denoising steps. Read it to connect variational learning, Gaussian noise, and score matching to the practical recipe that became the foundation of modern diffusion image models.

- Authors: Ho, Jain, and Abbeel
- Venue: NeurIPS 2020
- Track: ML Math
- Difficulty: Medium
- Reading time: 50 min

### [Auto-Encoding Variational Bayes](https://fanout.sh/daily/2026-07-24-auto-encoding-variational-bayes.md)

Auto-Encoding Variational Bayes makes latent-variable models trainable with ordinary backpropagation. Read it to see how the evidence lower bound, amortized inference, and the reparameterization trick turn an intractable posterior problem into a practical stochastic-optimization recipe.

- Authors: Kingma and Welling
- Venue: ICLR 2014
- Track: ML Math
- Difficulty: Hard
- Reading time: 55 min

### [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](https://fanout.sh/daily/2026-07-23-dropout.md)

Dropout is a compact lesson in turning randomness into regularization. Read it to see how masking neurons during training discourages brittle co-adaptation and approximates an ensemble without paying the cost of running many networks at inference time.

- Authors: Srivastava et al.
- Venue: JMLR 2014
- Track: ML Math
- Difficulty: Very Easy
- Reading time: 35 min

### [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://fanout.sh/daily/2026-07-22-direct-preference-optimization.md)

DPO turns language-model alignment from a multi-stage reinforcement-learning pipeline into a direct preference-learning objective. Read it to see how one mathematical reparameterization can remove the separate reward model and make preference tuning much simpler to train.

- Authors: Rafailov et al.
- Venue: NeurIPS 2023
- Track: AI Research
- Difficulty: Easy
- Reading time: 40 min

### [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://fanout.sh/daily/2026-07-21-pagedattention.md)

PagedAttention connects a familiar systems idea—virtual-memory paging—to one of LLM serving's hardest constraints: a large, dynamic KV cache. Read it to see how memory layout, sharing, and allocation policy turn directly into higher batching capacity and serving throughput.

- Authors: Kwon et al.
- Venue: SOSP 2023
- Track: Inference
- Difficulty: Hard
- Reading time: 55 min

### [From Tensor Buffer to Distributed Memory Hierarchy](https://fanout.sh/daily/2026-07-20-kv-cache-management-survey.md)

This survey treats the KV cache as a distributed memory-management problem, not just a temporary tensor. It is a useful July 20 pick for understanding why LLM serving depends on locality, lifetime, ownership, substrate, and the tradeoffs behind paged, shared, disaggregated, and tiered cache designs.

- Authors: Li, Wang, and Chen
- Venue: arXiv 2026
- Track: Inference
- Difficulty: Medium
- Reading time: 50 min

### [Attention Is All You Need](https://fanout.sh/daily/2026-07-19-attention-is-all-you-need.md)

The Transformer paper replaced recurrence with attention and made sequence modeling easier to parallelize. Read it for the architectural move that turned token-to-token routing into the core primitive behind modern language models.

- Authors: Vaswani et al.
- Venue: NeurIPS 2017
- Track: AI Research
- Difficulty: Medium
- Reading time: 45 min

### [FlashAttention](https://fanout.sh/daily/2026-07-18-flashattention.md)

FlashAttention keeps exact attention but changes the memory traffic pattern. It is a clean example of why practical model speed often comes from respecting hardware limits rather than changing the math.

- Authors: Dao et al.
- Venue: NeurIPS 2022
- Track: Inference
- Difficulty: Hard
- Reading time: 55 min

### [Scaling Laws for Neural Language Models](https://fanout.sh/daily/2026-07-17-scaling-laws.md)

The scaling-laws paper turns model size, data, and compute into a quantitative tradeoff. It is useful when you want to reason about progress curves instead of treating bigger training runs as folklore.

- Authors: Kaplan et al.
- Venue: arXiv 2020
- Track: ML Math
- Difficulty: Medium
- Reading time: 50 min

### [Training Compute-Optimal Large Language Models](https://fanout.sh/daily/2026-07-16-chinchilla.md)

Chinchilla is the scaling paper that made data budget feel as important as parameter count. It is a good daily pick for learning why a smaller model trained on more tokens can beat a larger undertrained one.

- Authors: Hoffmann et al.
- Venue: arXiv 2022
- Track: AI Research
- Difficulty: Medium
- Reading time: 45 min

### [Dynamo: Amazon's Highly Available Key-value Store](https://fanout.sh/daily/2026-07-15-dynamo.md)

Dynamo is the classic availability-first storage paper. It is worth reading for how partitioning, quorums, hinted handoff, vector clocks, and application-level conflict resolution combine into a product-shaped system.

- Authors: DeCandia et al.
- Venue: SOSP 2007
- Track: Systems
- Difficulty: Hard
- Reading time: 60 min

### [MapReduce](https://fanout.sh/daily/2026-07-14-mapreduce.md)

MapReduce shows how a constrained programming model can hide distribution, retries, shuffling, and locality. Read it for the system-design lesson that an API can be an operational strategy.

- Authors: Dean and Ghemawat
- Venue: OSDI 2004
- Track: Systems
- Difficulty: Easy
- Reading time: 40 min

### [In Search of an Understandable Consensus Algorithm](https://fanout.sh/daily/2026-07-13-raft.md)

Raft is the consensus paper to read before discussing leader election, replicated logs, or metadata coordination. It is intentionally structured for understandability, which makes it a strong beginner systems paper.

- Authors: Ongaro and Ousterhout
- Venue: USENIX ATC 2014
- Track: Systems
- Difficulty: Easy
- Reading time: 45 min

### [LoRA: Low-Rank Adaptation of Large Language Models](https://fanout.sh/daily/2026-07-12-lora.md)

LoRA is the cleanest first paper for understanding efficient model adaptation. It turns fine-tuning into small low-rank update matrices, which connects directly to Fanout's model training and serving material.

- Authors: Hu et al.
- Venue: ICLR 2022
- Track: Inference
- Difficulty: Easy
- Reading time: 35 min

### [BERT: Pre-training of Deep Bidirectional Transformers](https://fanout.sh/daily/2026-07-11-bert.md)

BERT is the pretrain-then-fine-tune paper that made bidirectional Transformer encoders the default for many language understanding tasks. It is useful for separating encoder intuition from decoder-only LLM intuition.

- Authors: Devlin et al.
- Venue: NAACL 2019
- Track: AI Research
- Difficulty: Medium
- Reading time: 45 min

### [Batch Normalization](https://fanout.sh/daily/2026-07-10-batch-normalization.md)

BatchNorm is a compact paper for learning how activation statistics, optimization stability, and architecture design interact. It is a good bridge between practical deep learning and the math of normalization.

- Authors: Ioffe and Szegedy
- Venue: ICML 2015
- Track: ML Math
- Difficulty: Medium
- Reading time: 35 min

### [Deep Residual Learning for Image Recognition](https://fanout.sh/daily/2026-07-09-resnet.md)

ResNet is the skip-connection paper that makes depth trainable in practice. It is beginner-friendly because the core idea is simple: learn a residual change instead of relearning the whole representation.

- Authors: He et al.
- Venue: CVPR 2016
- Track: AI Research
- Difficulty: Very Easy
- Reading time: 35 min

### [Efficient Estimation of Word Representations in Vector Space](https://fanout.sh/daily/2026-07-08-word2vec.md)

The Word2Vec paper is still one of the best first reads for representation learning. It shows how simple prediction tasks can turn words into vectors with useful geometric structure.

- Authors: Mikolov et al.
- Venue: ICLR Workshop 2013
- Track: AI Research
- Difficulty: Very Easy
- Reading time: 30 min

### [Sequence to Sequence Learning with Neural Networks](https://fanout.sh/daily/2026-07-07-sequence-to-sequence.md)

Seq2Seq is the clean predecessor to attention and Transformers. It is a good beginner paper because the encoder-decoder contract is easy to visualize and still appears in translation, summarization, and agents.

- Authors: Sutskever et al.
- Venue: NeurIPS 2014
- Track: AI Research
- Difficulty: Very Easy
- Reading time: 35 min

### [Adam: A Method for Stochastic Optimization](https://fanout.sh/daily/2026-07-06-adam.md)

Adam is the optimizer paper learners meet constantly in code before they understand it. The first pass is approachable: it combines momentum-like gradient averages with squared-gradient adaptation.

- Authors: Kingma and Ba
- Venue: ICLR 2015
- Track: ML Math
- Difficulty: Very Easy
- Reading time: 30 min

### [Dremel: Interactive Analysis of Web-Scale Datasets](https://fanout.sh/daily/2026-07-05-dremel.md)

Dremel is a strong systems pick for understanding columnar analytics, nested data, and query serving at scale. It connects directly to Fanout's storage and analytics material without becoming a database-product tour.

- Authors: Melnik et al.
- Venue: VLDB 2010
- Track: Systems
- Difficulty: Medium
- Reading time: 45 min

### [Bigtable: A Distributed Storage System for Structured Data](https://fanout.sh/daily/2026-07-04-bigtable.md)

Bigtable is the beginner-friendly bridge from simple key-value stores to distributed sorted maps. It teaches rows, tablets, locality, metadata, and why storage shape follows access patterns.

- Authors: Chang et al.
- Venue: OSDI 2006
- Track: Systems
- Difficulty: Easy
- Reading time: 45 min

### [The Google File System](https://fanout.sh/daily/2026-07-03-google-file-system.md)

GFS is a useful first distributed storage paper because the assumptions are explicit: large files, append-heavy workloads, commodity failures, and a master that keeps metadata tractable.

- Authors: Ghemawat et al.
- Venue: SOSP 2003
- Track: Systems
- Difficulty: Easy
- Reading time: 40 min

### [A Mathematical Theory of Communication](https://fanout.sh/daily/2026-07-02-mathematical-theory-of-communication.md)

Shannon's information theory paper is hard but worth anchoring early in the calendar. Entropy, bits, uncertainty, and channel capacity sit underneath compression, coding, language modeling, and evaluation intuition.

- Authors: Claude Shannon
- Venue: Bell System Technical Journal 1948
- Track: ML Math
- Difficulty: Hard
- Reading time: 65 min

### [The Perceptron](https://fanout.sh/daily/2026-07-01-perceptron.md)

The Perceptron is a good July 1 starting point because it gives learners the original linear-classifier idea before modern depth, attention, or scale enter the story.

- Authors: Frank Rosenblatt
- Venue: Psychological Review 1958
- Track: AI Research
- Difficulty: Very Easy
- Reading time: 30 min

---
This representation contains public Fanout content only. Protected Pro lessons, account data, billing, checkout, and pricing are not included.

Browse the public content map: https://fanout.sh/sitemap.md