'fid' 검색 결과

Drafts a one-page anonymous company profile for sell-side M&A outreach before a confidentiality agreement is signed. It organizes business highlights, financial metrics, and transaction details, then checks for information that could reveal the company’s identity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing an anonymous company profile for M&A outreach
• Gauging buyer interest before a confidentiality agreement
• Summarizing business strengths, financials, and deal terms

Analyze Donald Trump’s negotiations, public messaging, and decisions using frameworks drawn from public records. Compare possible next moves with probabilities, confidence levels, and unknown factors, or switch to roleplay to simulate his responses in a given situation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Interpreting negotiation demands and threats
• Comparing possible next moves with confidence levels
• Examining public statements and media messaging

Examine recorded calls and voicemails for signs of AI-generated speech. Extract acoustic features, classify samples with machine learning, and prepare reports with confidence scores and sound visualizations to support voice impersonation investigations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating calls impersonating executives
• Checking suspicious voicemails
• Comparing suspect recordings with genuine voice samples

Build threat actor profiles from public reports, social media, code repositories, and other intelligence sources. Correlates indicators, maps related infrastructure and attack techniques, and produces structured dossiers with confidence assessments about who may be responsible.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Consolidating public information about an attacker group
• Comparing aliases, targets, and motivations across sources
• Mapping related domains, IP addresses, and infrastructure

Collect indicators of compromise, technical clues such as suspicious domains and file hashes, from incident evidence. Add context and confidence scores, then prepare them for detection, blocking, and structured sharing with partners.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extracting threat indicators from incident evidence
• Adding threat context and confidence scores to indicators
• Preparing indicator lists for detection and blocking

Investigate suspicious IP addresses, domains, URLs, file hashes, and email artifacts using multiple threat intelligence sources. Assess confidence that each indicator is malicious, check links to known campaigns, and document evidence for blocking, monitoring, or allowing it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Triaging indicators from phishing emails
• Investigating suspicious IP addresses and URLs in alerts
• Assessing bulk indicators before adding them to blocklists

Investigate repository behavior, architecture, or possible causes without changing files. Trace relevant code and supporting material, then rank explanations by evidence and confidence, with concrete file references and clear distinctions between observations, conclusions, and unresolved questions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate possible causes of a bug
• Understand behavior across multiple files
• Explain architecture and tradeoffs

Checks factual claims in articles and reports against current sources or independently queried original data. Applies corrections, assigns one of six confidence levels to each claim, and produces a sourced report with an overall pass or fail assessment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking facts before publishing articles
• Verifying numbers, dates, and attributed quotes
• Rechecking reports generated from databases

Turn search results from chat, email, documents, and other sources into a coherent answer. Merge duplicate information, retain source references, highlight conflicting accounts, and reflect source freshness and authority when expressing confidence in the findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reconstruct a decision from chat, email, and documents
• Combine duplicate search results while preserving sources
• Identify conflicting accounts and changes over time

Turns user corrections and recurring mistakes into actionable rules with confidence scores. It records when each rule applies and why it matters, so relevant lessons can be retrieved before similar work begins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording a rule after a user correction
• Saving a lesson from a recurring mistake
• Keeping rules specific to one project

Build three-tier bookings forecasts from sales opportunities and historical conversion data. Project recurring revenue by customer cohort, assess the reliability of each sales stage, and present forecast numbers alongside their assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a quarterly bookings forecast for the board
• Projecting annual recurring revenue for the next 4–8 quarters
• Finding customer cohorts with declining revenue retention

Draft freelance contracts, project proposals, and confidentiality agreements with clauses tailored to US, EU, UK, or DACH contexts. Organize scope, payments, ownership, and deliverables in Markdown, with instructions for conversion to Word.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting a contract for a new freelance engagement
• Preparing a client proposal with budget and timeline
• Defining deliverables and acceptance criteria

Estimate development effort using affected code, design documents, dependencies, and past sprint results. Get optimistic, expected, and pessimistic day estimates, along with a confidence level, key risks, and a suggested breakdown into smaller tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimating effort before starting a feature
• Planning changes that span several systems
• Assessing uncertainty when sprint history is missing

Find open issues affected by a proposed code change. Identify additional problems it may fix or requested behavior it may conflict with, then report supporting code differences, issue symptoms, and confidence levels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find other open issues a PR may resolve
• Check whether a PR conflicts with requested behavior
• Prepare evidence for related-issue reviews

Turn selected research conversations or notes into themes supported by evidence and counterexamples. Distinguish observed behavior from preferences and hypotheses, assess confidence, and suggest a small next experiment with an observable measure of success.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding recurring themes across research interviews
• Comparing evidence, counterexamples, and confidence
• Planning a small experiment to test findings

Review PDF and Office documents for language errors, contradictions, confidential information, factual inaccuracies, and calculation mistakes. Produce an annotated copy with issue severity and suggested corrections, using external sources and calculations where needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Proofreading reports before sharing
• Checking figures and cited facts
• Finding confidential information before publication

Research topics across multiple sources, cross-check findings, and verify source URLs before producing a report with confidence labels. Choose from quick lookups to iterative investigations that preserve findings, with community sources prioritized for questions about public reactions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare AI technology trends
• Map a market and its products
• Investigate community reactions

Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Convert PDF, PowerPoint, Excel, and Word documents into structured Markdown for search and information extraction. Outputs include document metadata, a parsing confidence score, and the parser used, with fallback parsers available when earlier attempts fail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing documents for a searchable knowledge base
• Structuring contract text for information extraction
• Checking parsing confidence before manual review

Classify messages, rate content, or estimate probabilities using defined question types. Receive machine-readable answers, with confidence scores for classification and ratings, and apply the same questions to multiple inputs in a batch.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Routing customer inquiries to departments
• Rating support quality against defined criteria
• Classifying sentiment across multiple messages

Continue suitable tasks when web search repeatedly fails by assessing whether existing knowledge is sufficient. Mark unverified claims, distinguish confidence levels, and provide a follow-up checklist so readers can see what still needs checking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing whether work can continue without web search
• Drafting documents from established knowledge during search outages
• Marking unverified claims and confidence levels

Scan source code for security weaknesses with Semgrep after approving the target and rulesets. Choose full results or focus on important findings, then receive merged results and a record of failed or skipped scans.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit source code for security weaknesses
• Check for vulnerabilities before code review
• Focus on findings with medium-to-high confidence and impact

Plan authorized security tests involving phone calls, text messages, physical access, and USB devices. Helps develop scenarios from public information, document interactions, and assess how staff verification procedures and physical controls respond.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning authorized phone and SMS security tests
• Reviewing visitor and badge verification procedures
• Designing USB device security exercises

Design charts and dashboards that show data distributions, uncertainty, missing values, and sample sizes clearly. Choose visual forms that preserve meaningful variation and explain where averages, aggregation, or interval choices could mislead readers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing a chart to show a data distribution
• Presenting confidence intervals clearly
• Revealing variation hidden by dashboard averages