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Observation window
90 days
Published by official sources. Dates are shown in UTC.
Updated Oct 9, 2026, 01:42 PM UTC
Official updates
41
Published in this view
Companies publishing
1
Distinct companies in this window
Days with activity
27
Days with at least one update
Independent stories
0
0 verified US tech publishers
Cross-company mentions
0
Independent stories associated with another company
Open prediction markets
0
Separate from hiring rankings
The infographic
Redis official publishing pulse
Publication cadence
When the updates appeared
Daily publication dates in UTC. Bars count posts; the line counts distinct companies.
| Date UTC | Updates | Companies |
|---|---|---|
| 2026-07-12 | 0 | 0 |
| 2026-07-13 | 0 | 0 |
| 2026-07-14 | 0 | 0 |
| 2026-07-15 | 0 | 0 |
| 2026-07-16 | 0 | 0 |
| 2026-07-17 | 0 | 0 |
| 2026-07-18 | 0 | 0 |
| 2026-07-19 | 0 | 0 |
| 2026-07-20 | 0 | 0 |
| 2026-07-21 | 0 | 0 |
| 2026-07-22 | 2 | 1 |
| 2026-07-23 | 0 | 0 |
| 2026-07-24 | 0 | 0 |
| 2026-07-25 | 0 | 0 |
| 2026-07-26 | 1 | 1 |
| 2026-07-27 | 2 | 1 |
| 2026-07-28 | 2 | 1 |
| 2026-07-29 | 1 | 1 |
| 2026-07-30 | 0 | 0 |
| 2026-07-31 | 0 | 0 |
| 2026-08-01 | 0 | 0 |
| 2026-08-02 | 0 | 0 |
| 2026-08-03 | 2 | 1 |
| 2026-08-04 | 2 | 1 |
| 2026-08-05 | 1 | 1 |
| 2026-08-06 | 2 | 1 |
| 2026-08-07 | 0 | 0 |
| 2026-08-08 | 0 | 0 |
| 2026-08-09 | 1 | 1 |
| 2026-08-10 | 1 | 1 |
| 2026-08-11 | 0 | 0 |
| 2026-08-12 | 2 | 1 |
| 2026-08-13 | 1 | 1 |
| 2026-08-14 | 1 | 1 |
| 2026-08-15 | 0 | 0 |
| 2026-08-16 | 0 | 0 |
| 2026-08-17 | 2 | 1 |
| 2026-08-18 | 1 | 1 |
| 2026-08-19 | 0 | 0 |
| 2026-08-20 | 0 | 0 |
| 2026-08-21 | 0 | 0 |
| 2026-08-22 | 0 | 0 |
| 2026-08-23 | 0 | 0 |
| 2026-08-24 | 1 | 1 |
| 2026-08-25 | 0 | 0 |
| 2026-08-26 | 0 | 0 |
| 2026-08-27 | 0 | 0 |
| 2026-08-28 | 1 | 1 |
| 2026-08-29 | 0 | 0 |
| 2026-08-30 | 0 | 0 |
| 2026-08-31 | 0 | 0 |
| 2026-09-01 | 0 | 0 |
| 2026-09-02 | 0 | 0 |
| 2026-09-03 | 0 | 0 |
| 2026-09-04 | 0 | 0 |
| 2026-09-05 | 0 | 0 |
| 2026-09-06 | 0 | 0 |
| 2026-09-07 | 0 | 0 |
| 2026-09-08 | 1 | 1 |
| 2026-09-09 | 0 | 0 |
| 2026-09-10 | 0 | 0 |
| 2026-09-11 | 1 | 1 |
| 2026-09-12 | 0 | 0 |
| 2026-09-13 | 0 | 0 |
| 2026-09-14 | 1 | 1 |
| 2026-09-15 | 0 | 0 |
| 2026-09-16 | 0 | 0 |
| 2026-09-17 | 0 | 0 |
| 2026-09-18 | 0 | 0 |
| 2026-09-19 | 0 | 0 |
| 2026-09-20 | 0 | 0 |
| 2026-09-21 | 0 | 0 |
| 2026-09-22 | 6 | 1 |
| 2026-09-23 | 2 | 1 |
| 2026-09-24 | 1 | 1 |
| 2026-09-25 | 0 | 0 |
| 2026-09-26 | 0 | 0 |
| 2026-09-27 | 0 | 0 |
| 2026-09-28 | 0 | 0 |
| 2026-09-29 | 1 | 1 |
| 2026-09-30 | 1 | 1 |
| 2026-10-01 | 0 | 0 |
| 2026-10-02 | 0 | 0 |
| 2026-10-03 | 0 | 0 |
| 2026-10-04 | 0 | 0 |
| 2026-10-05 | 0 | 0 |
| 2026-10-06 | 0 | 0 |
| 2026-10-07 | 0 | 0 |
| 2026-10-08 | 1 | 1 |
| 2026-10-09 | 0 | 0 |
Source breadth
Who published most
Official updates from the most active companies in this view.
| Company | Updates |
|---|---|
| Redis | 41 |
Coverage map
Official posts and independent mentions
One story may mention several companies. Bars follow its publication date and count it once in this view.
| Date UTC | Official posts | Independent stories |
|---|---|---|
| 2026-07-12 | 0 | 0 |
| 2026-07-13 | 0 | 0 |
| 2026-07-14 | 0 | 0 |
| 2026-07-15 | 0 | 0 |
| 2026-07-16 | 0 | 0 |
| 2026-07-17 | 0 | 0 |
| 2026-07-18 | 0 | 0 |
| 2026-07-19 | 0 | 0 |
| 2026-07-20 | 0 | 0 |
| 2026-07-21 | 0 | 0 |
| 2026-07-22 | 2 | 0 |
| 2026-07-23 | 0 | 0 |
| 2026-07-24 | 0 | 0 |
| 2026-07-25 | 0 | 0 |
| 2026-07-26 | 1 | 0 |
| 2026-07-27 | 2 | 0 |
| 2026-07-28 | 2 | 0 |
| 2026-07-29 | 1 | 0 |
| 2026-07-30 | 0 | 0 |
| 2026-07-31 | 0 | 0 |
| 2026-08-01 | 0 | 0 |
| 2026-08-02 | 0 | 0 |
| 2026-08-03 | 2 | 0 |
| 2026-08-04 | 2 | 0 |
| 2026-08-05 | 1 | 0 |
| 2026-08-06 | 2 | 0 |
| 2026-08-07 | 0 | 0 |
| 2026-08-08 | 0 | 0 |
| 2026-08-09 | 1 | 0 |
| 2026-08-10 | 1 | 0 |
| 2026-08-11 | 0 | 0 |
| 2026-08-12 | 2 | 0 |
| 2026-08-13 | 1 | 0 |
| 2026-08-14 | 1 | 0 |
| 2026-08-15 | 0 | 0 |
| 2026-08-16 | 0 | 0 |
| 2026-08-17 | 2 | 0 |
| 2026-08-18 | 1 | 0 |
| 2026-08-19 | 0 | 0 |
| 2026-08-20 | 0 | 0 |
| 2026-08-21 | 0 | 0 |
| 2026-08-22 | 0 | 0 |
| 2026-08-23 | 0 | 0 |
| 2026-08-24 | 1 | 0 |
| 2026-08-25 | 0 | 0 |
| 2026-08-26 | 0 | 0 |
| 2026-08-27 | 0 | 0 |
| 2026-08-28 | 1 | 0 |
| 2026-08-29 | 0 | 0 |
| 2026-08-30 | 0 | 0 |
| 2026-08-31 | 0 | 0 |
| 2026-09-01 | 0 | 0 |
| 2026-09-02 | 0 | 0 |
| 2026-09-03 | 0 | 0 |
| 2026-09-04 | 0 | 0 |
| 2026-09-05 | 0 | 0 |
| 2026-09-06 | 0 | 0 |
| 2026-09-07 | 0 | 0 |
| 2026-09-08 | 1 | 0 |
| 2026-09-09 | 0 | 0 |
| 2026-09-10 | 0 | 0 |
| 2026-09-11 | 1 | 0 |
| 2026-09-12 | 0 | 0 |
| 2026-09-13 | 0 | 0 |
| 2026-09-14 | 1 | 0 |
| 2026-09-15 | 0 | 0 |
| 2026-09-16 | 0 | 0 |
| 2026-09-17 | 0 | 0 |
| 2026-09-18 | 0 | 0 |
| 2026-09-19 | 0 | 0 |
| 2026-09-20 | 0 | 0 |
| 2026-09-21 | 0 | 0 |
| 2026-09-22 | 6 | 0 |
| 2026-09-23 | 2 | 0 |
| 2026-09-24 | 1 | 0 |
| 2026-09-25 | 0 | 0 |
| 2026-09-26 | 0 | 0 |
| 2026-09-27 | 0 | 0 |
| 2026-09-28 | 0 | 0 |
| 2026-09-29 | 1 | 0 |
| 2026-09-30 | 1 | 0 |
| 2026-10-01 | 0 | 0 |
| 2026-10-02 | 0 | 0 |
| 2026-10-03 | 0 | 0 |
| 2026-10-04 | 0 | 0 |
| 2026-10-05 | 0 | 0 |
| 2026-10-06 | 0 | 0 |
| 2026-10-07 | 0 | 0 |
| 2026-10-08 | 1 | 0 |
| 2026-10-09 | 0 | 0 |
Official charts count the selected company’s own feed; independent mentions include stories from other publishers. Charts cover only ingested sources, not all market news. Stock prices and prediction markets provide context; neither proves a hiring change nor affects the hiring ranking.
Seven-day attention radar
Search and prediction signals
Google Trends shows a traffic bucket for a trending search cluster, not searches for this employer alone. Polymarket markets reflect their own question and trading activity. Neither is a hiring indicator.
No verified company matches for this signal filter yet. The monitor will keep checking.
Data source: Google Trends and Polymarket. Only exact company-name matches are shown; ambiguous names are excluded.
Chronological feed
Latest about Redis
41 stories · page 2 of 3
redis.io
Metadata filtering: boost search precision as data grows
Vector search is great at finding things that are semantically similar, but similarity isn't the same as correctness. A pure vector query doesn't know that a product is out of stock, that a document belongs to a different customer, or that a policy wa...
redis.io
Choosing & integrating LLM APIs: a practical guide
Making your first LLM API call is easy: with most provider SDKs, it's about five lines of code. Keeping that call fast, affordable, and reliable once real users show up is where the actual engineering happens: costs can compound as conversations grow,...
redis.io
Vector search in production: index trade-offs, failure modes & what to watch
Vector search runs on a simple idea: turn data into coordinates, and treat similarity as distance. An embedding model maps each sentence, image, or document to a point in a few hundred dimensions of space, where items with related meaning land near ea...
redis.io
Vector search database: news & 2026 guide
If you've built anything on top of an LLM in the past couple of years, you may have hit the wall many builders hit: the model writes fluently but has no view into your data. A vector search database helps close that gap. It stores vector embeddings an...
redis.io
Agent memory as a moat: how context compounds
Base LLM inference is stateless. The model doesn't remember your last conversation, your users' preferences, or the mistake your agent made ten minutes ago. Unless the app supplies persisted context, everything gets discarded after each request. That ...
redis.io
Fresh context: change data capture, not batch ETL
In many systems, the reason an agent quotes yesterday's data isn't the model. It's the pipeline behind it: a nightly ETL job that refreshed the agent's context hours ago. Change data capture (CDC) can shrink that staleness window from hours to seconds...
redis.io
Vector embeddings & language: how models turn words into geometry
A user types "refund policy" into your search box, but the doc they need is titled "returns and reimbursements." Keyword matching scores it near zero even though it's exactly what the user asked for. Vector embeddings help address this mismatch by rep...
redis.io
Reciprocal rank fusion: why combining search results is harder than it looks
You run a keyword search and get back a ranked list with Best Matching 25 (BM25) scores. You run a vector search over the same documents and get a second list with cosine similarities. You want to merge them into a single ranking that surfaces the mos...
redis.io
How Redis brings persistent memory to Snowflake Cortex Agents
AI agents can reason and act, but without memory, every interaction starts from zero. Intelligent short-term memory and persistent context across conversations are what turns a capable model into a truly useful agent. It should remember the useful det...
redis.io
Inference latency: what it measures & why it varies
Ask an engineer what their LLM app's inference latency is, and the honest answer is "which one?" The time to the first visible token, the time to the finished response, and the time an agent spends across a chain of calls are three different numbers. ...
redis.io
Top vector database alternatives for RAG pipelines
You're building an AI app: maybe a RAG system, an agent with memory, or a chatbot with semantic caching. You need vector search, and you're weighing your options. One is a unified real-time platform like Redis, which runs vector search alongside cachi...
redis.io
When does the A2A protocol actually matter?
If you're building multi-agent systems, someone has probably asked whether you're "doing A2A yet," with the implication that you should be. When teams actually reach for it, most can't say why they need A2A over MCP. A more useful question: do your ag...
redis.io
Semantic memory search for AI agents
Your AI agent handles a long onboarding conversation. The next day, it asks the same user for their name. That's not a bug. A language model keeps no memory of earlier calls, so without an external memory layer, each request starts fresh and the agent...
redis.io
Multi-agent observability: why one trace isn't enough
A single AI agent is usually easy to trace. One loop, one context window, one trace—you can read it top to bottom, spot the bad prompt or the failed tool call, and fix it. Multi-agent systems are different. Agents, shared memory, and external tools sp...
redis.io
Connect AI agents to data sources with Redis
An AI agent that can't reach your data is just a chatbot with opinions. Without runtime context, a model only knows its training data and whatever sits in the current prompt. So your app has to feed it production-specific facts at runtime: your produc...
redis.io
Context engineering for AI: what it is & how to build it
Your support agent confidently tells a customer they qualify for a refund under a 60-day return policy. Your actual policy is 30 days. The agent hallucinated the longer window, and the easy reaction is to blame the model. But the model never saw your ...
redis.io
Token-budget-aware LLM reasoning: cut costs in 2026
Reasoning models think before they answer, and those reasoning tokens are usually part of what you pay for. They're billed as output tokens, the expensive kind, and a single request can generate a few hundred of them depending on the problem. If your ...
redis.io
The 4 Failure Modes of Agent Context in Production
A production AI agent depends heavily on the context layer that tells it what to know at the moment it acts. It can pass every staging test, answer questions, call the right tools, and demo beautifully, then hit production and confidently offer a re...
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