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Compare 8 Instagram Analytics Tools for 2026
The guide compares eight Instagram analytics tools for 2026, arguing that native Instagram Insights is useful for a quick overview but offers limited history, exporting, and competitor comparison. It frames third-party platforms as a way to consolidate metrics such as reach, engagement, follower growth, Story performance, demographics, and hashtag results while reducing manual reporting. The listed tools serve different needs: Brandwatch and Sprout Social emphasize cross-platform management and reporting, Iconosquare offers 100+ Instagram metrics, Pallyy focuses on creators and posting-time recommendations, and Sendible targets agencies managing multiple profiles. Selection should follow goals and reporting requirements, with engagement rate, reach, follower growth, click-throughs, and conversions identified as core measures; competitor and influencer tracking is available in many tools.
LeRobot v0.4.0: Supercharging OSS Robot Learning
LeRobot v0.4.0 expands the open-source robot-learning stack with scalable datasets, simulation benchmarks, processing tools, policies, and hardware integrations. LeRobotDataset v3.0 introduces chunked episodes, video streaming, unified Parquet metadata, improved loading and memory efficiency, plus utilities for editing and converting datasets. A modular Processor system separates policy and robot pipelines, while direct Accelerate integration simplifies multi-GPU training; the release also adds LIBERO, Meta-World, PI0, PI0.5, and GR00T N1.5 support. A plugin architecture enables third-party robots, cameras, and teleoperators to be installed without changing the core library, with Reachy 2 and phone teleoperation included, alongside a self-paced Robot Learning Course and hands-on tutorial.
Platform Thinking: Three Views from Two Sigma Leaders
Two Sigma structures its quantitative investment operations around foundational platforms that balance operational speed with scientific rigor. In data engineering, foundational teams provide both raw and curated datasets using BigQuery, CI/CD-managed transformation pipelines, and reusable data contracts termed ice cubes to serve ninety percent of use cases. Quantitative modeling incorporates open-source large language models trained strictly on point-in-time data to rapidly generate features while preventing temporal leakage. Long-term platform innovation follows an epsilon and omega strategy that couples overarching vision with iterative, learning-focused steps. Platform leadership categorizes artificial intelligence applications into advisory, oracle, operational, and agentic functions designed to enhance human productivity rather than replace researchers.
Using a web-based POS with a mobile Terminal reader
Web-based POS systems can reuse ecommerce infrastructure for showroom-floor selling, but mobile readers such as the Stripe M2 and Tap to Pay cannot be accessed directly by a mobile browser. The proposed wrapper app avoids a complete native POS rebuild by embedding the site in a React Native WebView and connecting a mobile or Tap to Pay reader through the Stripe Terminal React Native SDK. A bridge message carries cart information, such as a cart ID and ready-to-pay status, to a ReaderManager that retrieves the amount and calls createPaymentIntent(), collectPaymentMethod(), and confirmPaymentIntent(). Payment results can return to the POS through a backend webhook and WebSocket or frontend polling, while security requires origin-checked messages, nonces, and server-side validation. The approach retains WebView performance, offline, native-access, debugging, and security limitations.
Building the Open Agent Ecosystem Together: Introducing OpenEnv
Meta and Hugging Face have partnered to launch OpenEnv Hub, an open platform and specification designed for agentic AI environments. Large language models require external tools and APIs to complete tasks, but exposing broad toolsets directly introduces significant safety and semantic challenges. In response, OpenEnv establishes sandboxed environments providing defined APIs, authenticated tool access, and isolation for reinforcement learning post-training and deployment pipelines. The initial release includes the OpenEnv 0.1 RFC series covering component architecture, packaging, communication, and Model Context Protocol tool encapsulation. Creators can run local Docker-based environments using standard step, reset, and close interfaces while integrating with frameworks like TorchForge, TRL, verl, and SkyRL.
We’re Hiring a Principal Backend Engineer to Shape the Future of Medium
Medium is hiring a Principal Backend Engineer, its most senior individual-contributor level, to lead the evolution of backend systems supporting reading, writing, and discovery for millions of users. The role combines hands-on engineering with organizational leadership: the hire will define backend strategy, set quality and architectural standards, review critical designs and code, mentor engineers, and partner with engineering, design, data, and executive leadership. Its technical focus includes modernizing a large living codebase, improving reliability and scalability, and balancing long-term platform evolution with building, debugging, and refining systems directly. Medium says the position should help teams move faster and ship with confidence while keeping the technology fast, stable, and built for longevity; it is fully remote in the US and seeks engineers experienced with systems used by millions, technical debt, fan-outs, and complex edge cases.
Corrosion
Corrosion is Fly.io’s open-source service discovery and state synchronization system, built to distribute routing data across globally deployed edge proxies and worker servers. It replaces centralized consensus-oriented designs with a Rust program that propagates a SQLite database through SWIM-based gossip and QUIC, using cr-sqlite’s CRDT tables and logical timestamps to reconcile updates without locking, a central server, or distributed consensus. The system’s design follows link-state routing ideas and exploits Fly.io’s fully connected WireGuard mesh, while worker-owned state keeps conflicts uncommon. The post also documents failures, including a contagious Rust RWLock deadlock, a nullable-column migration that triggered large-scale reconciliation, and the need for backups, whole-dataset republishing, testing, and regionalization to reduce blast radius. Corrosion now converges in seconds across thousands of workers.
Hugging Face and VirusTotal collaborate to strengthen AI security
Hugging Face announced a collaboration with VirusTotal to enhance the security of files shared across the Hugging Face Hub. Hosting more than 2.2 million public model and dataset repositories, the platform faces potential threats from malicious payloads, compromised files, and dependencies that execute unsafe code. Starting immediately, public repositories on the Hub undergo continuous scanning powered by VirusTotal threat intelligence. When users view repository or file pages, the Hub checks file hashes against VirusTotal without transferring raw file contents, preserving user privacy. Retrieved metadata displays detection counts, clean or malicious status, and known-bad relationships, enabling organizations to evaluate risks prior to downloading assets or integrating them into deployment pipelines.
Sentence Transformers is joining Hugging Face!
Sentence Transformers is officially transitioning its stewardship from the Ubiquitous Knowledge Processing Lab at TU Darmstadt to Hugging Face. The open-source embedding library was introduced in 2019 by Nils Reimers using a Siamese network architecture to overcome standard BERT limitations on sentence-level semantics. Tom Aarsen of Hugging Face has maintained the project since late 2023 and will continue leading its development. Under Hugging Face, the project retains its Apache 2.0 license and leverages infrastructure such as continuous integration and automated testing. Sentence Transformers currently serves over one million monthly unique users, with more than 16,000 community models publicly hosted on the Hugging Face Hub.
Modernising Grab’s model serving platform with NVIDIA Triton Inference Server
Catwalk, Grab’s machine learning model serving platform, accumulated technical debt by maintaining separate inference engines for ONNX, PyTorch, TensorFlow, and vLLM, contributing to higher latency, lower throughput, and rising costs. The team evaluated NVIDIA Triton Inference Server and began consolidating those engines around it, initially adopting ONNX. The Triton manager combines a server manager, which retrieves and verifies models, prepares configurations, launches Triton, and monitors health, with a proxy that translates legacy API requests without requiring client code changes. Benchmarks showed at least five times the traffic capacity for a transformer model; rollout reached over 50% of online deployments in 10 days, while some larger models saw p90 latency fall from 120 ms to 20 ms.
Supercharge your OCR Pipelines with Open Models
Modern vision-language OCR models can transcribe handwritten text, multiple scripts, mathematical expressions, and chemical formulas while also interpreting tables, charts, and images. The guide compares open OCR models and explains how output choices—including DocTags, HTML, Markdown, and structured JSON—affect digital reconstruction, LLM input, and programmatic processing. It also covers locality awareness through anchors or bounding boxes, prompt-based task switching, fine-tuning versus out-of-the-box use, and factors for selecting a model. Practical deployment options include local or managed inference, vLLM, Hugging Face Endpoints, and batch scripts that add OCR results to datasets. The discussion extends document AI beyond transcription to visual retrieval and direct document question answering with vision-language models, especially where converted text can lose layout or visual context.
Unlock the power of images with AI Sheets
Hugging Face released an update to AI Sheets, an open-source tool for transforming and enriching datasets without code. The update adds vision support powered by Inference Providers, enabling users to upload images, extract structured data, generate visuals, and perform image edits in a spreadsheet interface. Each column can execute AI actions configured with custom prompts and selected models. For text extraction and reasoning tasks, AI Sheets supports vision language models including Qwen/Qwen2.5-VL-7B-Instruct and Qwen/Qwen3-VL-235B-A22B-Reasoning, while image transformations use models like Qwen-Image-Edit. Users can iterate on prompts, provide feedback using thumbs-up ratings to create few-shot examples, and export completed datasets to the Hugging Face Hub, CSV, or Parquet.
AI Interview Success: An Interviewer's Inside Guide
Canva conducts AI-assisted programming interviews designed to evaluate candidates' engineering fundamentals amplified by artificial intelligence tools. Successful candidates structure their interviews across planning, development, and discussion phases, clarifying architectural assumptions before writing code and critically reviewing AI-generated output. Interviewers evaluate engineering problem-solving, technical depth, code ownership, and the effective collaboration with AI assistants rather than isolated prompting mechanics or rushed feature completion. Ineffective strategies include treating models as hands-off solution generators, rushing unvetted features, and prioritizing demonstrations of advanced prompting over core technical judgment. Preparation involves setting up project structures in advance, exploring requirements aloud, and remaining ready to justify architectural trade-offs and production scalability.
AI for Food Allergies
Global food allergies affect an estimated 220 million individuals, yet computational progress in biomedical discovery remains constrained by fragmented and inaccessible scientific data. The AI for Food Allergies initiative addresses this challenge by establishing an open research community and releasing the curated Awesome Food Allergy Datasets collection across multiple biological layers. Computational pipelines leverage deep learning models, such as AllergenAI and NetAllergen-1.0, which incorporate sequence motifs and computationally predicted MHC class II presentation propensities to evaluate allergenicity. Additionally, molecular property prediction benchmarks like QM9 provide high-accuracy quantum-mechanical properties for approximately 134,000 molecules, supporting generative and virtual screening workflows targeting IgE–FcεRI binding. These combined efforts systematically structure molecular, clinical, and chemogenomic resources to accelerate allergy diagnostics and therapeutic protein engineering.
Introducing Agent Skills
Agent Skills are folders containing instructions, scripts, and resources that Claude loads when relevant to specialized tasks, enabling custom expertise across Claude apps, Claude Code, and the API. The system scans available skills, loads only the minimal information and files needed, and can combine multiple skills automatically. Skills may include executable code, and API use requires the Code Execution Tool beta; developers can manage versions through the /v1/skills endpoint and Claude Console. Users can build skills with the skill-creator workflow or install them through the anthropics/skills marketplace, while organization-wide management and a partner-built directory were added in the December 18, 2025 update. The post presents Skills as composable, portable, efficient, and powerful, but cautions that code-executing skills should come from trusted sources to protect data.
Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face
Intel and Hugging Face benchmarked OpenAI GPT OSS text generation to evaluate performance on Google Cloud C4 virtual machines powered by Intel Xeon 6 processors against previous-generation C3 instances. The benchmark evaluated the unsloth/gpt-oss-120b-BF16 Mixture of Experts model across batch sizes from 1 to 64 using bfloat16 precision, static KV cache, and SDPA attention. To eliminate redundant computation, Hugging Face merged an optimization directing each expert sub-network to process only its routed tokens rather than all tokens. Testing demonstrated that C4 instances deliver 1.4x to 1.7x higher throughput per vCPU compared to C3 instances. At a batch size of 64, C4 achieved a 1.7x Total Cost of Ownership improvement alongside lower hourly pricing.
Get your VLM running in 3 simple steps on Intel CPUs
Deploying Vision Language Models locally presents computational challenges despite providing data privacy and independence from external servers. Optimum Intel and OpenVINO address this by optimizing lightweight models like SmolVLM2-256M for low-resource hardware, including Intel CPUs and GPUs. The process involves exporting the model to the OpenVINO Intermediate Representation format, applying 8-bit weight-only or static quantization, and executing inference with the transformed model. In single-image CPU benchmarks on an Intel Core Ultra 7 265K processor, OpenVINO with 8-bit weight-only quantization reduced time to first token from 5.150 seconds under PyTorch down to 0.247 seconds. The optimized configuration also raised decoding throughput from 0.722 to 63.928 tokens per second.
Nemotron-Personas-India: Synthesized Data for Sovereign AI
Most open artificial intelligence datasets reflect Western norms and English-only contexts, limiting model effectiveness across India's multilingual and multi-script landscape. To address this shortage of culturally grounded training data, NVIDIA released Nemotron-Personas-India under a CC BY 4.0 license. The synthetic dataset contains 21 million personas across 3 million records spanning 7.7 billion tokens in English, Devanagari Hindi, and Latin-script Hindi. The generation pipeline utilized NeMo Data Designer, an Apache-2.0 licensed Probabilistic Graphical Model for demographic grounding against 2011 Census distributions, and GPT-OSS-120B for narrative text. All records are entirely synthetic to eliminate re-identification risks while enabling fine-tuning for region-aware AI agents and domain-specific copilots.
Understanding Etsy’s Vast Inventory with LLMs
Etsy’s seller-created inventory spans more than 100 million items and thousands of categories, making it difficult to capture attributes such as color, material, or niche properties without global SKUs. Because sellers often provide descriptions, titles, and photos instead of complete structured fields, Etsy uses LLMs to distill unstructured listing data into attributes for search filters, comparisons, and other experiences. The pipeline combines seller data, expert-selected few-shot examples, taxonomy logic, and category rules; parallel prompts are routed through LiteLLM, then responses are validated with Pydantic dataclasses and monitored with tracing, metrics, alerts, and performance checks. Human-reviewed silver labels support evaluation, while target categories increased complete attribute coverage from 31% to 91%, and inferred attributes improved search-filter engagement and overall post-click conversion.
Cars24 Improves Search For 300 Million Users With MongoDB Atlas
Online car marketplace Cars24 previously operated a dual architecture where relational databases like Postgres synchronized data into separate search engines like Elasticsearch. As service offerings expanded globally for 300 million users, multiple engineering teams piping data into a single search index caused severe synchronization challenges, operational overhead, and developer friction. To simplify this infrastructure, Cars24 migrated to MongoDB Atlas hosted on Google Cloud, utilizing integrated Apache Lucene-powered MongoDB Atlas Search directly within the database layer. This architectural shift eliminated synchronization pipelines while accelerating search feature delivery through a unified API. Cars24 also replaced its ArangoDB geospatial search deployment with MongoDB Atlas, achieving robust multi-document ACID transactions across shards and cutting operational costs by 50%.
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